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Handbag Forum
Coach Inc. Is Dead. Long Live Tapestry.
The plan by Coach Inc. to rename itself Tapestry Inc. follows its acquisitions of Stuart Weitzman and Kate Spade.
At Coach Inc., which is famous for its leather goods but has become the holding company of an increasingly ambitious fashion group, the name of the game is now … Tapestry.
On Wednesday, the accessible-luxury group that owns Coach, Stuart Weitzman and Kate Spade announced its intentions to change the name of its parent to Tapestry Inc., the better to express its new shape as a multibrand entity with a variety of unique properties, as opposed to one dominated by a single brand.
"It's a wonderful metaphor for what we believe in, which is individual threads of different colors all working together to create a picture," said Victor Luis, the chief executive, waxing a little poetic.
Such semantic change has become something of a corporate trend. The Coach Inc. rebranding follows Google's decision to restructure and name its holding company Alphabet in 2015 and Tribune Publishing Company's reinvention as Tronc last year. Next up will reportedly be the Weinstein Company, as it attempts to distance itself from its disgraced co-founder, Harvey Weinstein.
But in Coach's case, the change also reflects what has become an escalating race to create the first American Fashion Group — or, as Mr. Luis styles it, "the first New York Fashion Group."
The name change, after all, follows Coach's 2015 acquisition of the Stuart Weitzman shoe label for up to $574 million and its purchase of Kate Spadefor $2.4 billion in May. And in July, Coach's rival Michael Kors acquired Jimmy Choo (a brand that Coach was reportedly also considering acquiring) for $1.2 billion. John Idol, the Kors chief executive, told The New York Times that it was "the beginning of a strategy that we have for building a luxury group that really is focused on international fashion brands."
Tapestry sends a message that "this is a home that is not limited to any category, channel or geography," said Victor Luis, the chief executive.
By rebranding Coach Inc., Mr. Luis is hoping to send a signal to potential targets in the $80 billion global premium fashion market that "this is a home that is not limited to any category, channel or geography."
While Mr. Luis declined to say how many more threads he anticipated adding to his particular tapestry, he did note that one of the requirements for the new name was that it demonstrate inclusivity.
"We embrace our differences, whether they be race, gender, sexual orientation or belief systems," he said. Mr. Luis added that while Tapestry was currently composed of brands based in the United States, he was open to acquisitions in Europe and Asia.
To that end, the name was also intended to clarify the differences between not only Tapestry and Kors but also Tapestry and potential European competitors like LVMH Moët Hennessy Louis Vuitton (the owner of brands like Vuitton, Dior, Givenchy and Fendi, and the world's largest luxury group by sales), Kering (Balenciaga, Yves Saint Laurent and Gucci, among others) and Richemont (Chloé, Alaïa, Cartier and Van Cleef & Arpels).
"I'm not here because I'm anyone's son," Mr. Luis said by way of example — an apparent veiled swipe at François-Henri Pinault, the chief executive of Kering, whose father, François Pinault, founded that group.
Then he said, "We are not a group that believes there is a single country where products have to be designed or manufactured," a reference to the insistence by European groups on the importance of origin — that there is equity in "made in France" or "made in Italy." Finally, Mr. Luis said Tapestry would include the results of each of its brands in its annual reports, as opposed to lumping them together into a single entity, as LVMH and Kering do with some of their brands.
Still, Tapestry is following in Kering's steps in at least one way: In 2013, the French group, too, changed its name (it had been PPR), to reflect its transformation from retail and luxury to a luxury and sports lifestyle group.
Mr. Luis said he was open to acquisitions in Europe and Asia.
That choice involved an invented word (pronounced like "caring") that refers to the word for "home" in Brittany, France, where Mr. Pinault's family is from. It was in line with other notable rebrandings like Andersen Consulting's switch to Accenture (a shortening of "accent on the future") and Kraft's move to Mondelez (a combination of "monde" and "deliz").
Mr. Luis said it was important for Coach to find a name that, unlike those moves, "wasn't too corporate-y or made up, that was easy for everyone to understand."
The search took two to three months and was conducted by the Carbone Smolan Agency. A list of thousands of names was winnowed to "tens," which were then tested for legality and cultural associations in the brands' key global markets.
"We were surprised Tapestry was still available," Mr. Luis said. The one catch: a concern that tapestries could be seen as old-fashioned, and possibly even elitist, associated as they are with European history and palaces. Ultimately, though, the word's suggestions of craft and handwork outweighed the negatives.
Of course, there is at least one other reference associated with "Tapestry."
"For anyone who is aware of the album, Carole King does come up," Mr. Luis acknowledged, referring to the 1971 record. (He admitted that he had it at home.) "But we discovered most millennials had not heard of it."
Besides, it is not turning "Tapestry" into its theme music. "We will not be playing it in the office," he said.
The name change officially goes into effect Oct. 31. Tapestry will trade on the New York Stock Exchange under the symbol TPR, as opposed to the former COH.
Coach no more; New York company wants to be called Tapestry
NEW YORK (AP) — To better incorporate all of the brands it now owns, the storied Coach company of New York is changing its name to Tapestry.
The luxury goods company that came to prominence in the "Mad Men" era now owns brands like Stuart Weitzman and Kate Spade & Co. as well. CEO Victor Luis said Wednesday that the name Tapestry is more inclusive.
Coach acquired Stuart Weitzman in 2015 in a deal valued up to $574 million. It spent $2.4 billion for Kate Spade this year, seeking to broaden its appeal.
"We are now at a defining moment in our corporate reinvention, having evolved from a mono-brand specialty retailer to a true house of emotional, desirable brands," Luis said in a company release.
A website with the new name, which becomes official at the end of the month, is up and running.
The change is part of Coach's pursuit of younger shoppers who may not feel the same draw to store windows on Manhattan's 5th Avenue.
Coach began as a small workshop in Manhattan in 1941, and became a fashion powerhouse in the early 1960s though innovate designs.
Coach Inc. will also be changing its ticker symbol on the New York Stock Exchange from "COH," to "TPR." Shares of the company declined more than 2 percent to $39.11 in morning trading
13 Editors' Picks From Kate Spade's Surprise Sale (Including This Ice Cream Cone Purse)
ALERT — this is not a drill! Kate Spade New York just launched one of its two-day surprise sales, where everything is up to 75 percent off — and it's all perfect for spring and summer. (Seriously, there's an ice cream cone–shaped wristlet.) Scroll to see Stylish editors' top 13 picks!
Kate Spade New York Foster Court Kirk
Kate Spade New York Arbour Hill Alston
Kate Spade New York Kirk Park Saffiano Lacey
Kate Spade New York Eat Cake for Breakfast Metro Watch
Kate Spade New York Rose-Colored Glasses Rose Intarsia Sweater
Kate Spade on the hunt for a potential buyer
It's a bag-eat-bag world.
Kate Spade is shopping for a potential buyer after an activist investor prodded the handbag maker, suggesting that another management team could squeeze more growth out of the brand, according to the Wall Street Journal.
On Wednesday, Kate Spade shares jumped 23 percent to $17.86 on reports that it was considering a sale.
Just six weeks ago, hedge fund Caerus Investors sent a letter to Kate Spade's board urging it to consider a sale. The fund, which manages roughly $250 million and counts the accessories label as one of its top investments, has been invested in Kate Spade since 2009 when it was owned by Liz Claiborne.
Caerus argued that the company would be better off under the control of a global player that could help it expand in the US and abroad.
"The board deserved a wake-up call to come to its senses and realize there are other paths to realizing value," Ward Davis of Caerus told The Post.
Since the letter was sent, Davis said that he and his colleague Brian Agnew have had a "constructive dialogue with key members of the board."
"If Kate Spade is truly contemplating a process to explore alternatives or a sale, Caerus would support that," Davis and Agnew told The Post, declining to confirm any knowledge of the reported deal talks.
Reps for Kate Spade did not immediately respond to requests to comment.
Kate Spade lowers 2016 forecast, stock drops 18%Michael Kors and Coach are among the most obvious potential buyers, industry experts say.
"Kate Spade is a strong brand that should be larger, and the notion of how to become larger is a fair question," says Instinet retail analyst Simeon Siegel.
Kate Spade has long been a favorite among hedge fund investors. Jana Partners, Citadel, AQR, Balyasny and Point72 each have roughly 1 percent stakes in the company, according to data compiled by Bloomberg.
"The product resonates very strongly," Siegel said, pointing to a Kate Spade collaboration with Everpurse last year on a bag that charges an iPhone. It sold out.
Kate Spade's stock has been choppy, falling 30 percent since July compared with an 8 percent rise in the S&P 500, Wells Fargo analyst Ike Boruchow pointed out in a Wednesday research note.
Flavor of the Month – Kate Spade Ice Cream Francis Tote
Kate Spade has become pretty well-known for her unusual designs. They typically come in collections inspired by a given theme, where each piece is different but related to that theme. The pieces are playful and unique, sometimes even taking on the shape of the object it is trying to portray. But as much as I love the idea of a clutch shaped like a gnome, sometimes you want your playful piece to be somewhat practical. That is why I like the Kate Spade New York Ice Cream Francis Tote!
This particular piece is actually part of the Flavor of the Month collection, which depicts what I can only refer to as "old fashioned" fun. The collection features bags that resemble a carousel, a bag of peanuts, a theater or carnival ticket, and an ice cream truck. All of these pieces are clutch-sized and are made to look like these objects. The Francis Tote in this collection, however, is just that… a tote. It has a fun, ice cream and Popsicle print that reminds me a little bit of the prints Dooney and Bourke used to do. I really like the colors and, despite the brightness, I think this piece would be practical for a casual summer day.
Spring Color Bag Trends: Baby Blue Handbags by Botkier, Minkoff, and Kors
With spring just around the corner, bring on the colorful handbag trends. I am so excited for spring and even more excited for all of the spring colors. As we learned in my first post in this series (Spring Color Trends: Petal Pinks), Easter egg colored handbags are popping up everywhere and they are a great way to welcome spring. So what's next? When I think of spring, I think of many things including flowers. However I also think of something that some people may not associate with spring — clear blue skies. There is just something about a spring sky that makes me feel connected with the world around me. It is sort of serene in a way. It makes sense to me then that a light blue handbag would have that same calming effect. In fact, I know it does! That is why I am showcasing some lovely baby blues here. You know, to provide you with a little inspiration.
Botkier Trigger Saddle Bag in Sky
Botkier is not a designer that I often associate with pastels. But the Trigger Saddle Bag sure does look inviting in this lovely shade of blue! I love the color and the complimenting silver tone hardware.
The saddle-shaped Botkier shoulder bag in glazed leather with polished studs. Long pulls hang from the magnetic front flap, and a slim pocket outfits the back. Wraparound zip at the gusset. Lined, 2-pocket interior. Adjustable shoulder strap. $248
Rebecca Minkoff Micro Perry Satchel in Bleached Blue
Rebecca Minkoff does many colors well. Pastels though, have always been one of my favorites. They always look good on Rebecca Minkoff styles though and light blue is no exception.
Zips adjust the volume of this scaled-down Rebecca Minkoff satchel rendered in pebbled leather. Concealed magnets secure the side pockets, and the top zip opens to a lined, 4-pocket interior. Double handles and optional, adjustable shoulder strap. $325
MICHAEL Michael Kors Jet Set Multifunction Tote in Sky
Pastels do not have to come on small handbags. They look great on larger styles too, especially baby blue. It is such a versatile color, it works with more conspicuous styles like this tote.
A large, versatile MICHAEL Michael Kors tote in saffiano leather. The top zip opens to a logo-lined interior with 6 pockets and a middle zip compartment. Adjustable handles. $208
HOW TO SPOT A REAL GIVENCHY ANTIGONA HANDBAG
Riccardo Tisci's Givenchy is all about the combination of high fashion, street style and gothic romanticism. Since he took over as Creative Director in 2005 he's transformed the label in his image, garnered a coterie of famous friends and turned out collections filled with must-have items. Case in point: the Antigona handbag. "Launched in 2011, Riccardo Tisci designed this duffle to mimic the Givenchy aesthetic: clean design with strong lines and geometric ornamentation," says Senior Director of Authentication Graham Wetzbarger. "Knowing his customers' lifestyle, Tisci designed this bag to be extremely functional and durable."
Sleek but roomy enough in its larger sizes to fit all of your essentials, the Antigona is coveted by street style stars and celebrities. Often seen on the arms of Rosie Huntington-Whiteley, Kate Moss, Olivia Palermo and most Kardashians, it's something of an It bag equalizer — edgy but very pulled together. It comes in three sizes — Mini, Small and Medium — and is available in a long list of colorways, prints, exotic skins and more, so while the design is iconic, you can choose a version that fits your personal style. As is often the case with highly sought-after handbags, fake versions abound, so it's important to know the signs of an authentic Antigona. Here, Wetzbarger breaks down the telltale signs of the real thing.
1. Givenchy Antigona Materials & Construction
"Givenchy uses various leathers from polished cowhide to goat, but it should always be sturdy and structured, which keeps the bag from losing its shape," says Wetzbarger. "While rigid, it should still have a smooth, luxurious hand, and appear supple rather than plasticy. The triangular leather logo detail is padded with batting so it should be convex and puffy, not flat. It should also be firmly affixed without any gaps. Each letter should be separate in the Givenchy logo and very tightly affized to the bag. If you examine closely, you should see that they are very cleanly finished with no rough or raw edges."
On the bottom of the bag, you should find 'feet' consisting of two panels created from stacked layers of leather that are glued together and stitched down. "Inspect these carefully," warns Wetzbarger. "Some fakes will just have stitching in the shape of these panels." An authentic Antigona is lined with solid cotton twill and should match the color of the leather on other interior details like the label and zipper pocket. "While not fixed to the interior with adhesive, the lining is well-constructed to fit perfectly inside the dimensions of the handbag. It should not be loose or bunchy and should have one center seam across the bottom," Wetzbarger says.
"If you have the original dustbag, it should read 'Givenchy' or 'Givenchy Paris' on white cotton twill fabric. It should have a crisp screen print with well-defined lines and edges, not bubbles or raised areas of ink."
2. Givenchy Antigona Brand Stamping & Serial Numbers
The date code format used on Givenchy Antigona handbags follows LVMH standards. According to Wetzbarger: "The first two characters denote the location at which the bag was manufactured, and are followed by four numbers; the first and third digits represent the week and the second and fourth represent the year in which the bag was made."
"3C" denotes the workshop location. This Antigona was made in the twelfth week of 2012.
He suggests cross referencing your date code with the seasonal specifics of you Antigona. "While some colors like black are made every season," he says, "certain colorways are season-specific, so you can check to see if particular bags are from the correct year." In fact, some fake date codes are so wrong that they indicate that the bag was made in a year prior to the Antigona's debut, or a year in the future. "FW 2011 is the season the Antigona premiered, so your date code should fall between then and now," notes Wetzbarger.
3. Givenchy Antigona Hardware
Handbag aficionados will be familiar with zipper brands such as Riri and Lampo, however the Givenchy Antigona features an oversized zipper made by Raccagni. "Within roughly the past year however, Givenchy has begun using zippers branded with their own logo," Wetzbarger explains.
The geometric shape of the bag is echoed in both the unique pyramid-shaped leather zipper pull and strap hardware; the latter should be emblazoned with the Givenchy logo on one side of the D-Ring. Wetzbarger suggests inspecting the color of the hardware which should be "silver or very light gold in color, but nothing brassy."
HOW TO TELL IF YOUR CÉLINE HANDBAG IS THE REAL THING
BY JODY HUME
Designer Phoebe Philo has a penchant for designing It bags. At the helm of Céline, she's breathed new life into the once flagging house with designs that have generated waitlists around the world. The first Céline It bag was the Luggage Tote, and was soon followed by other popular designs like the Trapeze, Phantom, Trio, Box and Edge bags.
As Philo remarks of the women who are fans of her designs, "They are all different, but with the common thread of an appreciation for clothes that suggest something new but made to last far beyond fast-trend fashion." While Céline's bags appeal to lovers of minimalism with their clean lines, sleek colorblocking and lack of obvious logos, they are also instantly recognizable to those in the know. The popular flared gusset designs of Philo's bags for Céline have spawned lookalikes from both luxury and contemporary brands across the industry, so taking a close and careful look at Céline handbags to ensure their authenticity is important.
When inspecting Céline, there are several telltale signs of an item's authenticity.
1. Heat Stamping
On a Mini Luggage Tote, the exterior Céline logo should have an accent aigu, or right-pointing accent, above the first "e," and "Paris" printed beneath. The font should not look thick and blurry, nor thin and faint. The interior label will have "Made In Italy" printed in caps to the right. Note that on different size totes, the labels will differ. On Nano Luggage Totes, the logo is embossed on a leather tab.
2. Serial Number
In most Céline bags, you'll find a leather serial number tab. The embossed numbers are not unique and can be repeated. They should always follow this format: one letter, two letters, four numbers. Be wary of serial numbers beginning in "S-GA" as counterfeiters often use this prefix. The corners of the rectangular tab are normally clipped at clean right angles. Neither the tab nor the corners should ever be curved.
3. Zipper
Unlike many other fashion houses, Céline does not use brand name zippers in their bags. The backs should be plain and unmarked. However, the size of the zipper is often listed on the end, i.e. the number eight. On Luggage Totes, the zippers should have an antiqued finish and should never be brassy or shiny. The D-Rings on the zippers should be large enough to leave space on either side of the leather pull.
4. Lining
Céline always uses high-quality, sturdy leather on their handbags, which lends to their hefty weight. Smooth calfskin Luggage Totes will have matching leather linings. Drummed or pebbled leather bags will be lined in microsuede. The Céline Trio is quite a versatile accessory, as it can be separated into three different bags. If you want, you can even mix and match different colors.
Spray-On Nail Polish Just Got Even Cooler
Written by TAYLOR BRYANT
Remember back in November when we reported on Nail Inc.'s new spray-on nail polish and our entire office, along with the rest of the internet, proceeded to lose its freaking mind? Well, we didn't think it was possible, but Milk Makeup is now taking the craze to even wilder extremes with spray-on nail art.
Oh, snap.
The edgy new makeup brand, launched by Milk Studios, is releasing its own version of spray-can polish, along with a collection of nail stencils to help you create intricate designs, cool ombré effects, and even a French manicure (don't laugh, it's making a serious comeback), all with the aim of a nozzle and the press of a button.
The nail design renaissance is showing no signs of slowing down, which is why it makes perfect sense to speed the polishing process up. Milk Makeup is the self-professed friend of the "on-the-go" gal, and nothing says "get out the door" faster than an artsy manicure that you can apply in seconds.
We got our hands on a bottle before it hit the market, and are happy to report that it follows Milk's ethos. The pigment applies easily without any streaks or bubbles — something that has plagued even the most meticulous polisher. What's more is that it dries super fast and the excess pigment around your nails washes off easily with soap and water. (Full disclosure: you may need a Q-tip with some nail polish remover to get off some of the color that's super close to your nails.) Fun to use and basically fool-proof? We're sold.
The polish, priced at $12, is available for pre-sale today on Urban Outfitters. Until you can get your hands on a bottle, we suggest checking out the video below to tide you over.
Leo Takes Home An Oscar, $885 Sweatshirt Sells Out
Written by ERIN CUNNINGHAM
We're calling it now: The "Leonardo DiCaprio effect" is officially a thing.
On the heels of the 41-year-old actor's first Academy Award win, one item has gone viral and sold out completely — and we're not talking about Trefoils Girl Scout cookies.
In September, Vetements (a Kanye West favorite), known for its sartorial rule-breaking, showed a variation of its now-insanely-popular, super-long-sleeved sweatshirt with an image of young Leo and Kate from Titanic screen-printed on it. At the time, a nod to the '97 blockbuster may have seemed random. But, now that DiCaprio is making headlines (and rightfully so), the terry cloth hoodie seems like it hit shelves at just the right time.
Despite its $885 price tag, the item has sold out completely across the internet (on sites like SSENSE, MatchesFashion, La Garçonne, Dover Street Market, Antonioli, and Selfridges), and we're thinking his win has a little something to do with it. Though we haven't received official confirmation that this was the reason behind things (we'll update this spot when we do hear back), but we're betting the odds are likely. (Remember: The lengths fangirls will go to knows no bounds.)
If you're a die-hard DiCaprio fan hoping to get your hands on this Demna Gvasalia-designed piece, the slightly cheaper, hooded T-shirt version is still available on SSENSE in every size except large — though we're betting that stock won't be around for long. And for those who cannot. live. without. a sweatshirt emblazoned with Leo's young mug, there are a few overpriced options up for auction on eBay. One bit of advice: Once you get your hands on this inevitable collector's item, never let go.
Bagity.com for all your Fashion Accessories needs


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We just want to thank you so much for a great job on planning our wedding. To organise everything ourselves would have been a nightmare. But with your assistance, we had a bigger and better wedding than we had ever hoped for.
You answered every question we had, any phone call, attended any consultations and on a more personal note you were a 'Maid of Honour' figure for me as my girls were not near by.
You are truly awesome at what you do, and you can turn any fairytale wedding into a real life storey. You were the first and last wedding planner we had spoke to and we hired you on the spot. As you would agree we had a lot in common and you knew everything we wanted and turned it from a thought into a reality.
Again Tina, we just want to thank you for your assistance on making our special day one to remember. Not only did we get the most beautiful wedding from you but we have also made a friend.
I just wanted to write to you to give you a massive THANK YOU!!!
You made my wedding day so perfect, from the gorgeous beach decorations through to the absolutely stunning reception. I was truly amazed at the beautiful beach setting, and transformation when I walked though the door at the Surf Club…WOW!!! Your honesty and laid back approach, along with constant updates and confirmations, made all the preparations stress free. I want to thank you (and your husband!) again for making my day truly special, you did a brilliant job!
Just a quick note to thank you for the fabulous job you did organising the beach and table decorations for my daughters wedding on 10 November.
As I only have one daughter, I sincerely thank you for making her day so wonderful. You did a terrific job and I couldn't recommend you enough to anyone who is organising a wedding.
Just wanted to touch base and say thank you for all your help for making our wedding day so memorable. The marquee was perfect for the heat, can you believe people got sunburnt!!!!!
The whole decoration was "awesome" and the fact that it was "stress free", setup and taken down by professionals. All our family and friends commented on how fabulous the decorations were.
The personalized care and attention to detail that you provided is second to none! Tina you were our first choice for our wedding ceremony and reception decorations and we could not have been happier! Our guests commented on how fantastic the ceremony set-up was, and our reception room was transformed into a fantastic cocktail reception area. Tina you were so accommodating and professional, and our big day would not have been as amazing as it was without you! Thank-you so much!
Just wanted to say thanks again on the great job you did. It looked amazing and I still have guests complimenting me on how beautiful it all looked…..
Thank you so much for the amazing work you did at the Powerboat Club – you completely transformed it into the most beautiful setting for our wedding. The ceremony was gorgeous, those petals really topped it off – it was just beautiful! It totally exceeded all my expectations. Thank you for taking away any disappointment we had in not being able to have the ceremony outdoors as planned – it turned out perfect as it was!
I will happily recommend you to all my engaged friends and anyone else who asks – you really made our day perfect. And you know they say it's good luck if it rains on your wedding day – so after Saturday's weather – I think Phil and I have lots of luck coming our way!!
Thank you so much again for everything you did to make our day as special as it was – it really wouldn't have been the same without your help.
We just wanted to say thank-you soooo much for the beautiful ceremony set up at Cotton Tree on the10th March.
Everyone said that everything looked perfect and, which we are both very grateful for. It was hard organizing our wedding from a different state, especially as we didn't get a chance to meet until 2 days before the wedding, but everything all fell into place nicely. Even when we changed the ceremony site at the last minute! But we were very happy with the location on the day, especially that the weather held out.
Thanks again for all your help in the lead up to our wedding – we really appreciate all the time and effort that you put in.
Brent even mentioned that you helped to calm him down before the ceremony, so thanks for that also.
"I have just returned from my honeymoon and I wanted to say a huge thank you Tina for helping me to organise such a fun and memorable wedding. The guests were really bowled over!
I had originally planned on organising everything myself right down to every little decoration on the table, but soon realised what a stressful day I would have (not to mention almost impossible time wise). Using your services to prepare the wedding ceremony and decorations was some of the best money I spent on my wedding. You had plenty of creative ideas that fitted perfectly with my vision and offered advice that was practical and at times even cost saving to me. I found as the wedding got closer it was a real comfort to know there was someone that knew what I wanted and could take charge, while I relaxed and focused on enjoying my day. I found as the wedding got closer I became more reliant on you for advice or help with last minute oversights that I had forgotten. You were always helpful, professional and courteous, and I appreciate all the little extras that you add in with your service. I have already started recommending you to others!
I wish you continued success in your business. You certainly deserve it, and obviously enjoy, and are dedicated to what you do. Thanks again, you were a star!
Thanks for the photos they look great! Thank you so much for setting up our ceremony it looked beautiful and just what we wanted. The time and effort you put into making sure our wedding was just the way we wanted made all the difference.
I still wanted to write to say how happy both Ben and I were with our day. We were incredibly lucky to have Princess Events & Wedding Co-Ordinators help with our wedding. I'd like to thank you for the excellent service you provided, the entire day was a whole lot of fun and the end results were astounding!
From the first time we met, you were professional, friendly and reliable. We cannot thank you enough, without you; our day would not have been as amazing as it was! You gave us exactly the look we desired for our wedding day, it was a really beautiful experience.
Thank you for your very important part in it!
Thank you so much for all the help you have given to make our wedding such a wonderful experience. I am so happy with how everything was set up. The ceremony and reception looked beautiful. I couldn't have asked for more. It was how I envisioned it.
The Ceremony looked fantastic like a fairytale. Again, thank you so much.
Dan & I would like to thank you for everything you did in making our wedding day so special! I could not have asked for more when it came to the ceremony, it looked amazing! Thank you once again and good luck for the future.
You have done an absolutely amazing job on our wedding. What a wonderful exciting day we had and everyone commented on the set up at the ceremony and reception. I can't thank you enough!
This is just a quick email to pass on our sincerest thanks for the beautiful ceremony and reception on Saturday. Everything was perfect, above and beyond all that we had hoped for. You did a fantastic job and we just wanted to thank you for making our special day perfect and for all the support and advice that you have offered us during the planning of the wedding.
A very belated thank you for everything you did to make Our Wedding spectacular. Arriving at Penny Lane Park and seeing what you had created just took my breath away. It was absolutely perfect! So did the Surf Club – it looked just magical.
You are a very talented person who is also full of fun and laughter, which makes things run a little more smoothly.
We will be recommending you to the world, and if we ever need a decorator again, you are the person we will call. Basically we can't say thank you enough.
Take care Tina & a million thank you's again.
Thankyou so much for everything you did for us. The day was perfect and the ceremony set-up looked amazing. We couldn't of wished for a better day!
Back at work after a fantastic week in the Whitsundays. We really loved the day, especially the ceremony was beautiful for us, thank you so much for everything – I'm looking forward to seeing some photos of the ceremony and reception before everyone was there as I'm told by Julian it looked great.
Thank you so much for my ceremony decorations, they were perfect and matched the theme that I had imagined for my wedding.
Thank you also for all of your advice leading up to the wedding, it was invaluable.
Also, a Big thank you for changing the ceremony location at the last minute & sorry for the confusion.


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\section{Introduction}
\label{introduction}
Style transfer, or to repaint an existing image with the style of another, is considered as a challenging but interesting task in both academia and industry. Recently, the pioneering works of Gatys \etal~\cite{gatys2015neural,gatys2015texture,gatys2016image} have proved that the correlations (\ie, Gram matrix) between feature maps extracted from a pre-trained deep convolutional neural network (DCNN) can represent the style of an image well. Since then, significant efforts have been made to improve in many aspects including efficiency~\cite{ulyanov2016texture,johnson2016perceptual,li2016precomputed}, quality~\cite{li2016combining,wang2017multimodal,liao2017visual,gu2018arbitrary}, generality~\cite{chen2017stylebank,dumoulin2017learned,huang2017arbitrary,li2017universal,sheng2018avatar,lu2019closed}, user control~\cite{champandard2016semantic,gatys2017controlling} and photorealism~\cite{luan2017deep,li2018closed,yoo2019photorealistic}, etc. However, despite the remarkable success, these methods often neglect an important aspect, \ie, the diversity, since many of the applications (\eg, art creation and creative design) are required to satisfy the preferences of different users.

In terms of diversity, one common explanation is that, image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same content and style, just like the results generated by different methods could all be visually pleasing and perceptually correct. However, the lack of meaningful variations in vanilla style transfer mechanism~\cite{gatys2016image,johnson2016perceptual,ulyanov2016texture} hampers the emergence of diversity, as the optimization-based methods often converge to the similar local optimum, while the feed-forward networks only produce fixed outputs for the fixed inputs.

Although challenging and meaningful, unfortunately, this problem has barely received enough attention and there are only a few efforts to solve it. For instance, based on the feed-forward networks, Li \etal~\cite{li2017diversified} introduced a diversity loss that penalized the feature similarities of different samples in a mini-batch. Ulyanov \etal~\cite{ulyanov2017improved} minimized the Kullback-Leibler divergence between the generated distribution and a quasi-uniform distribution on the Julesz ensemble~\cite{julesz1981textons,zhu2000exploring}. Although their methods could generate diverse texture samples or stylized images to a certain extent, they still suffer from three main limitations. (1) Restricted generalization. Once trained, their feed-forward network is tied to a specific style, which cannot be generalized to other styles. (2) Limited diversity. Since their diversity is learned by penalizing the variations in mini-batches of a finite dataset and the weight of diversity loss should be set to a small value, the degree of diversity is limited. (3) Poor scalability. Extending their approaches to other methods requires the intractable modifications to training strategies and network structures, which might be useful for some learning-based methods like~\cite{huang2017arbitrary}, but not suitable for recent learning-free methods~\cite{li2017universal,sheng2018avatar,li2018closed} as these methods transfer arbitrary styles in a style-agnostic manner.

Facing the aforementioned challenges, we rethink the problem of diversity and an important insight we will use is that a Gram matrix~\cite{gatys2016image}, which is widely used as the style representation of an image, can correspond to an infinite number of different feature maps, and the images reconstructed from these feature maps are the diverse results we are looking for. Obviously, the problem of diversity has now been transformed into the problem of how to obtain the different feature maps with the same Gram matrix. Inspired by the work of Li \etal~\cite{li2017universal} which decomposes the Gram matrices and separates the matching of them by whitening and coloring transforms (WCTs), we propose a simple yet effective method, \ie, deep feature perturbation (DFP), to achieve diversified arbitrary style transfer. Our diversity is obtained by using an orthogonal noise matrix to perturb the image feature maps extracted from a DCNN while keeping the original style information unchanged. That is to say, although the perturbed feature maps are different from each other, they all have the same Gram matrix. For ease of understanding, we regard Gram matrix as the style representation, and define that different feature maps with the same Gram matrix share the same style-specific feature space.

In this work, our DFP is based on the framework of WCT~\cite{li2017universal}, so it can be easily incorporated into many WCT-based methods~\cite{li2017universal,sheng2018avatar,li2018closed} and empower them to generate diverse results without any extra learning process. Note that this learning-free process is fundamentally different from the aforementioned diversified methods that require learning with pre-defined styles. Therefore, our method is able to achieve diversified arbitrary style transfer.

The main contributions of this work are threefold:

•	We propose to use deep feature perturbation, \ie, perturbing the deep image feature maps by an orthogonal noise matrix while keeping the original style information unchanged, to achieve diversified arbitrary style transfer.

•	Our method can be easily incorporated into existing WCT-based methods~\cite{li2017universal,sheng2018avatar,li2018closed} which are used for different style transfer tasks, \eg, artistic style transfer, semantic-level style transfer and photo-realistic style transfer.

•	Theoretical analysis proves the capability of the proposed method in generating diversity, and the experimental results demonstrate that our method can greatly increase the diversity while maintaining the quality of stylization.

\section{Related Work}

{\bf Gram-based Methods.} Gatys \etal~\cite{gatys2015neural,gatys2015texture,gatys2016image} first proposed an algorithm for arbitrary style transfer and texture synthesis based on matching the correlations (\ie, Gram matrix) between deep feature maps extracted from a pre-trained DCNN within an iterative optimization framework, but one major drawback is the inefficiency. To address this, Johnson \etal~\cite{johnson2016perceptual} and Ulyanov \etal~\cite{ulyanov2016texture,ulyanov2017improved} directly trained feed-forward generative networks for fast style transfer, but these methods need to retrain the network every time for a new style, which is inflexible. For this limitation, some methods~\cite{dumoulin2017learned,zhang2018multi,chen2017stylebank,li2017diversified,shen2018neural} were proposed to incorporate multiple styles into one single network, but they are still limited in a fixed number of pre-defined styles. More recently, Huang and Belongie~\cite{huang2017arbitrary} further allowed arbitrary style transfer in one single feed-forward network.

{\bf WCT-based Methods.} Recently, Li \etal~\cite{li2017universal} have proposed to exploit a series of feature transforms to achieve fast arbitrary style transfer in a style learning-free manner. They reformulated the task of style transfer as an image reconstruction process, with the feature maps of the content image being {\em whitened} at intermediate layers with regard to their style statistics (\ie, Gram matrix), and then {\em colored} to exhibit the same statistical characteristics of the style image. This method is essentially a Gram-based method, but it splits the Gram matrices by matrix decomposition, and separates the matching of them by whitening and coloring transforms (WCTs), thus providing an opportunity for our deep feature perturbation. Furthermore, Sheng \etal~\cite{sheng2018avatar} combined it with style swap~\cite{chen2016fast} for higher quality semantic-level style transfer. Li \etal~\cite{li2018closed} and Yoo \etal~\cite{yoo2019photorealistic} developed this to fast photo-realistic style transfer. More recently, Li \etal~\cite{li2018learning} derived the form of transformation matrix theoretically and directly learned it with a feed-forward network. Lu \etal~\cite{lu2019closed} derived a closed-form solution by treating it as the optimal transport problem. In our work, taking the most representative ones~\cite{li2017universal,sheng2018avatar,li2018closed} as examples, the proposed method can be easily integrated into the learning-free WCT process and empower these methods to generate diverse results, which will be shown in Section~\ref{exp}.

{\bf Diversified Methods.} Our method is closely related to~\cite{li2017diversified} and~\cite{ulyanov2017improved}. Li \etal~\cite{li2017diversified} introduced a diversity loss to allow the feed-forward networks to generate diverse outputs. It explicitly measures the variations in visual appearances between the generated results, and penalizes them in a mini-batch. Ulyanov \etal~\cite{ulyanov2017improved} proposed a new formulation that allowed to train generative networks which sampled the Julesz ensemble~\cite{julesz1981textons,zhu2000exploring}. Specifically, the diversity term of its learning objective is similar to that of  Li \etal~\cite{li2017diversified}, which quantifies the lack of diversity in the batch by mutually comparing the generated images. Although these methods could generate diverse outputs to a certain extent, they still suffer from the restricted generalization, limited diversity and poor scalability, as we have introduced in Section~\ref{introduction}.

The proposed method is based on WCT~\cite{li2017universal}, and can be easily integrated into WCT-based methods to empower them to generate diverse results. Unlike the previous diversified methods~\cite{li2017diversified,ulyanov2017improved} that need to train an independent network for every style, our diversity is learning-free and suitable for arbitrary styles. Moreover, without extra constraints, our method can generate an infinite number of solutions with satisfactory quality as well as distinct diversity.
\begin{figure*}[htbp]
	\centering
	\includegraphics[width=1\linewidth]{figs/1.jpg}
	\caption{Our diversified arbitrary style transfer pipeline. (a) We add an orthogonal noise matrix ${\bf Z}$ to perturb the whitening and coloring transform (WCT). Like~\cite{li2017universal}, the VGG and DecoderX are first trained for image reconstruction and then fixed for style transfer. $C$ and $S$ denote the content image and style image, respectively. (b) Our perturbed whitening and coloring transform (PWCT) can be applied in every level of the multi-level stylization framework of~\cite{li2017universal}.
	}
	\label{fig:fig1}
\end{figure*}

\section{Style-Specific Feature Space }
\label{SSIS}
Defining the style of an image is a quite tricky problem, and so far no unified conclusion has been reached. Informally, a style can be regarded as a family of visual attributes, such as color, brush strokes and line drawing, etc. Recently, Gatys \etal~\cite{gatys2015neural,gatys2015texture,gatys2016image} have proposed a new style representation (Gram matrix) for artistic images. In their works, the style of an image is represented by the correlations between deep feature maps extracted from a pre-trained DCNN. Given an image $\vec{x}$ as input, the vectorized feature map extracted from a certain layer (we only take one layer as an example) of the VGG model~\cite{simonyan2014very} is denoted as $F\in \mathbb{R}^{C\times HW}$, where $H$, $W$ are the height and width of the original feature map, $C$ is the number of channels. The style of the image $\vec{x}$ can be represented as follows:
\begin{equation}
G_{ij}=\sum_{k}F_{ik}F_{jk}=FF^T\in \mathbb{R}^{C\times C},
\label{gram}
\end{equation}
where $F_{ik}$ and $F_{jk}$ are the activations of the $i^{th}$ and $j^{th}$ filter at position $k$, $F^T$ is the transpose matrix of $F$.

It is obvious that, for a definite Gram matrix $\mathcal{G}$, there could be a large number of feature maps corresponding to it. Let $\mathcal{F}_l$ denote the vectorized feature map of an image in layer $l$. $\mathcal{F}_l$ is perceived as the style $\mathcal{G}$ in layer $l$ if its Gram matrix matches $\mathcal{G}$. Formally, given the loss function:
\begin{equation}
\mathcal{L}_\mathcal{G}(\mathcal{F}_l)=||\mathcal{F}_l\mathcal{F}_l^T-\mathcal{G}||,
\end{equation}
we define the feature maps that satisfy the following constraint belong to the same style-specific feature space of $\mathcal{G}$.
\begin{equation}
\mathcal{S}_\mathcal{G}=\{\mathcal{F}_l\in \mathbb{F}: \mathcal{L}_\mathcal{G}(\mathcal{F}_l)=0\},
\label{sen}
\end{equation}
where $\mathbb{F}$ is a set of feature maps. Features belonging to the same $\mathcal{S}$ are perceptually equivalent in style characteristics.

In particular, sometimes we do not need their Gram matrices to be exactly equal, and then we can get the relaxed constraint,
\begin{equation}
\mathcal{S}_\mathcal{G}^{\epsilon}=\{\mathcal{F}_l\in \mathbb{F}: \mathcal{L}_\mathcal{G}(\mathcal{F}_l)\leq \epsilon\},
\label{rsen}
\end{equation}
in which the feature maps are approximately equivalent in style characteristics.

In this work, our deep feature perturbation can easily achieve the first constraint (Eq.~(\ref{sen})), while the methods \cite{li2017diversified,ulyanov2017improved} only satisfy the second constraint (Eq.~(\ref{rsen})). That is to say, the Gram matrices of the diverse perturbed feature maps obtained by our method can be completely equal.

\section{Deep Feature Perturbation}
\label{DFP}
Our deep feature perturbation (DFP) is based on the work of Li \etal~\cite{li2017universal} and incorporated into its whitening and coloring transform (WCT) process to help generate diverse stylized results. The pipeline of our method is shown in Fig.~\ref{fig:fig1}, where the diversified style transfer is mainly achieved by the perturbed whitening and coloring transform (PWCT), which consists of two steps, \ie, whitening transform and perturbed coloring transform.

\begin{table*}
	\caption{Quantitative comparisons between single-level perturbation and multi-level perturbation in terms of run-time, tested on images of size $512\times 512$ and a 6GB Nvidia 980Ti GPU.}
	\centering
	\hspace{2cm}
	\setlength{\tabcolsep}{0.25cm}
	\begin{tabular}{ccccccccccc}
		\toprule
		Fig.~\ref{fig:fig2}&Li \etal~\cite{li2017universal}&I5&I4&I3&I2&I1&I5+I4&I5+I1&I3+I2+I1&I5+I4+I3+I2+I1\\
		\midrule
		Time/sec&3.01&{\bf 3.53}&3.51&3.04&3.03&3.02&4.14&3.54&3.05&4.15\\
		\toprule
		Fig.~\ref{fig:fig3}&Li \etal~\cite{li2018closed}&-&I4&I3&I2&I1&I4+I3&I4+I1&I2+I1&I4+I3+I2+I1\\
		\midrule
		Time/sec&0.29&-&{\bf 0.32}&0.31&0.30&0.29&0.33&0.32&0.30&0.34\\
		\bottomrule
	\end{tabular}
	\label{tab1}
\end{table*}

{\bf Whitening Transform.} Given a pair of content image $I_c$ and style image $I_s$, we first extract their vectorized VGG feature maps $F_c=\Phi(I_c)\in \mathbb{R}^{C\times H_cW_c}$ and $F_s=\Phi(I_s)\in \mathbb{R}^{C\times H_sW_s}$ at a certain layer $\Phi$ (\eg, $Relu\_3\_1$), where $H_c$, $W_c$ ($H_s$, $W_s$) are the height and width of the content (style) feature, and $C$ is the number of channels. We first center $F_c$ by subtracting its mean vector $m_c$. Then the whitening transform (Eq.~(\ref{eq5})) is used to transform $F_c$ to $\hat{F_c}$, in which the feature maps are uncorrelated from each other (\ie, $\hat{F_c}\hat{F_c}^T=I$).

\renewcommand\arraystretch{0.5}
\begin{figure}[t]
	\centering
	\setlength{\tabcolsep}{0.01cm}
	\begin{tabular}{cp{0.05cm}ccccc}
		\includegraphics[width=0.161\linewidth]{figs/2/c.jpg}&&
		\includegraphics[width=0.161\linewidth]{figs/2/i5.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i4.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i3.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i2.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i1.jpg}\\
		\scriptsize Content&&\scriptsize I5&\scriptsize I4&\scriptsize I3&\scriptsize I2&\scriptsize I1\\

		\includegraphics[width=0.161\linewidth]{figs/2/s.jpg}&&
		\includegraphics[width=0.161\linewidth]{figs/2/li.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i54.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i51.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i321.jpg}&
		\includegraphics[width=0.161\linewidth]{figs/2/i54321.jpg}\\
		\scriptsize Style&&\scriptsize Li~\etal~\cite{li2017universal}&\scriptsize I5 + I4&\scriptsize I5 + I1&\scriptsize I3 + I2 + I1&\fontsize{5.8pt}{\baselineskip}\selectfont{I5+I4+I3+I2+I1}\\
	\end{tabular}
	\caption{Single-level perturbation vs. Multi-level perturbation. Our DFP is integrated into method~\cite{li2017universal}. The top row shows the results obtained by only perturbing a single-level stylization in Fig.~\ref{fig:fig1}(b). The bottom row shows the results obtained by perturbing stylizations in multiple levels.
    }
    \label{fig:fig2}
\end{figure}

\begin{equation}
\hat{F_c}=E_cD_c^{-\frac{1}{2}}E_c^TF_c,
\label{eq5}
\end{equation}
where $D_c$ and $E_c$ are obtained by the singular value decomposition (SVD) of the Gram matrix $F_cF_c^T\in\mathbb{R}^{C\times C}$ (Eq.~(\ref{gram})), \ie, $F_cF_c^T=E_cD_cE_c^T$. $D_c$ is the diagonal matrix of the eigenvalues, and $E_c$ is the corresponding orthogonal matrix of eigenvectors.

{\bf Perturbed Coloring Transform.} We first center $F_s$ by subtracting its mean vector $m_s$. The coloring transform used in~\cite{li2017universal} is essentially the inverse of the whitening step, \ie, using Eq.~(\ref{eq6}) to transform $\hat{F_c}$ so that we can obtain $\hat{F_{cs}}$ which satisfies the same Gram matrix of $F_s$ (\ie, $\hat{F_{cs}}\hat{F_{cs}}^T=F_sF_s^T$).
\begin{equation}
\hat{F_{cs}}=E_sD_s^{\frac{1}{2}}E_s^T\hat{F_c},
\label{eq6}
\end{equation}
where $D_s$ and $E_s$ are obtained by the SVD of the Gram matrix $F_sF_s^T\in\mathbb{R}^{C\times C}$, \ie, $F_sF_s^T=E_sD_sE_s^T$. $D_s$ is the diagonal matrix of the eigenvalues, and $E_s$ is the corresponding orthogonal matrix of eigenvectors.

The goal of coloring transform is to make the Gram matrix of $\hat{F_{cs}}$ the same as that of $F_s$. According to our analysis in Section~\ref{SSIS}, these two feature maps share the same style-specific feature space. In theory, $\hat{F_{cs}}$ should have a large number of possibilities, but Eq.~(\ref{eq6}) only produces one of them. In order to traverse these solutions as much as possible, we propose to use deep feature perturbation.

\renewcommand\arraystretch{0.5}
\begin{figure}[t]
	\centering
	\setlength{\tabcolsep}{0.01cm}
	\begin{tabular}{cp{0.05cm}cccc}
		\includegraphics[width=0.195\linewidth]{figs/3/c.jpg}&&
		\includegraphics[width=0.195\linewidth]{figs/3/i4.jpg}&
		\includegraphics[width=0.195\linewidth]{figs/3/i3.jpg}&
		\includegraphics[width=0.195\linewidth]{figs/3/i2.jpg}&
		\includegraphics[width=0.195\linewidth]{figs/3/i1.jpg}\\
		\scriptsize Content&&\scriptsize I4&\scriptsize I3&\scriptsize I2&\scriptsize I1\\

		\includegraphics[width=0.195\linewidth]{figs/3/s.jpg}&&
		\includegraphics[width=0.195\linewidth]{figs/3/li.jpg}&
		\includegraphics[width=0.195\linewidth]{figs/3/i43.jpg}&
		\includegraphics[width=0.195\linewidth]{figs/3/i41.jpg}&
		\includegraphics[width=0.195\linewidth]{figs/3/i4321.jpg}\\
		\scriptsize Style&&\scriptsize Li~\etal~\cite{li2018closed}&\scriptsize I4 + I3&\scriptsize I4 + I1&\scriptsize I4 + I3 + I2 + I1\\
	\end{tabular}
	\caption{Another comparison of Single-level and Multi-level perturbation. Our DFP is integrated into method~\cite{li2018closed}. This method only uses four-level stylizations. The top row shows the results obtained by only perturbing a single-level stylization. The bottom row shows the results obtained by perturbing stylizations in multiple levels.
	}
	\label{fig:fig3}
\end{figure}

The key idea of our deep feature perturbation is incorporating an orthogonal noise matrix into Eq.~(\ref{eq6}) to perturb the feature $\hat{F_{cs}}$ while preserving its Gram matrix. Obviously, there are three places to insert the noise matrix, \ie, between $D_s^{\frac{1}{2}}$ and $E_s^T$, between $E_s^T$ and $\hat{F_c}$, and on the right side of $\hat{F_c}$ (since $E_s^TE_s=I$ and $\hat{F_c}\hat{F_c}^T=I$). We eventually insert the orthogonal noise matrix between $D_s^{\frac{1}{2}}$ and $E_s^T$ as this may consume the least computation and run-time (we will discuss this in Section~\ref{abstudy}).

We first obtain a random noise matrix $N$ (\eg, sampled from the standard normal distribution, we will discuss it in Section~\ref{abstudy}) according to the shape of $D_s^{\frac{1}{2}}$ and $E_s^T$. Assume that the shape of $D_s^{\frac{1}{2}}$ is $(C-k)\times(C-k)$, where $k$ is the number of small singular values (\eg, less than $10^{-5}$, Li \etal~\cite{li2017universal} suggest removing these small singular values to obtain higher quality results), and the shape of $E_s^T$ is $(C-k)\times C$, then the shape of $N$ is $(C-k)\times(C-k)$. To obtain orthogonal noise matrix, we apply the SVD to decompose $N$, \ie, $N=E_nD_nV_n^T$, and directly use the orthogonal matrix ${\bf Z}=E_n\in \mathbb{R}^{(C-k)\times (C-k)}$. Finally, we insert ${\bf Z}$ between $D_s^{\frac{1}{2}}$ and $E_s^T$ of Eq.~(\ref{eq6}). Our new perturbed coloring transform is formulated as follows:
\begin{equation}
\hat{F_{csn}}=E_sD_s^{\frac{1}{2}}{\bf Z}E_s^T\hat{F_c},
\label{eq7}
\end{equation}
since ${\bf ZZ}^T=I$, we can deduce as follows:
$$\hat{F_{csn}}\hat{F_{csn}}^T=(E_sD_s^{\frac{1}{2}}{\bf Z}E_s^T\hat{F_c})(\hat{F_c}^TE_s{\bf Z^T}D_s^{\frac{1}{2}}E_s^T)$$

$=E_sD_s^{\frac{1}{2}}({\bf Z}E_s^T\hat{F_c}\hat{F_c}^TE_s{\bf Z^T})D_s^{\frac{1}{2}}E_s^T$
\vspace{8pt}

$=E_sD_sE_s^T=F_sF_s^T$
\vspace{8pt}

\renewcommand\arraystretch{0.5}
\begin{figure}[t]
	\centering
	\setlength{\tabcolsep}{0.03cm}
	\begin{tabular}{cccccc}
		\includegraphics[width=0.16\linewidth]{figs/4/0_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/2_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/4_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/6_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/8_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/10_1.jpg}\\

		\includegraphics[width=0.16\linewidth]{figs/4/0_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/2_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/4_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/6_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/8_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/4/10_2.jpg}\\

		\scriptsize $\lambda=0$&\scriptsize $\lambda=0.2$&\scriptsize $\lambda=0.4$&\scriptsize $\lambda=0.6$&\scriptsize $\lambda=0.8$&\scriptsize $\lambda=1.0$\\
	\end{tabular}
	\caption{Trade-off between diversity and quality by varying diversity hyperparameter $\lambda$ in method~\cite{li2017universal} (+ our DFP).
	}
	\label{fig:fig4}
\end{figure}
In our later experiments, we find that only using our perturbed coloring transform may reduce the quality of stylization. This may be because $\hat{F_{cs}}$ (Eq. (\ref{eq6})) contains not only style information (Gram matrix) from $E_sD_s^{\frac{1}{2}}E_s^T$, but also some {\em content information from} $\hat{F_c}$ (Eq. (\ref{eq5})). Although our feature perturbation (Eq. (\ref{eq7})) can keep the style information unchanged, the content information may be affected by the noise matrix, which is manifested as a decline in quality. Fortunately, in WCT-based methods~\cite{li2017universal,sheng2018avatar,li2018closed}, the content information in $\hat{F_c}$ is {\em not the determinant} of the content in the final result, as in these methods $\hat{F_{cs}}$ is mainly served as the style feature, and blended with the content feature $F_c$ to balance the style and content (similar to our Eq. (\ref{eq9})). In order to increase the diversity while maintaining the original quality, we introduce a diversity hyperparameter $\lambda$ to provide user controls on the trade-off between them.
\begin{equation}
\hat{F_{csn}}'= \lambda \hat{F_{csn}}+(1-\lambda)\hat{F_{cs}}.
\label{eq8}
\end{equation}

Then, we re-center the $\hat{F_{csn}}'$ with the mean vector $m_s$ of the style, \ie, $\hat{F_{csn}}'=\hat{F_{csn}}'+m_s$. At last, we blend $\hat{F_{csn}}'$ with the content feature $F_c$ before feeding it to the decoder.
\begin{equation}
\hat{F_{csn}}'= \alpha \hat{F_{csn}}'+(1-\alpha)F_c,
\label{eq9}
\end{equation}
where the hyperparameter $\alpha$ serves as the weight for users to control the stylization strength, like~\cite{li2017universal}.

{\bf Multi-level Stylization.} We follow the multi-level coarse-to-fine stylization used in~\cite{li2017universal}, but replace their WCTs with our PWCTs, as shown in Fig.~\ref{fig:fig1} (b). In fact, we do not need to add noise to every level. We will discuss this in Section~\ref{abstudy}.

{\bf Discussions.} As a matter of fact, optimizing the diversity loss of~\cite{li2017diversified,ulyanov2017improved} can be viewed as a sub-optimal approximation of our method, as analyzed in Section~\ref{SSIS}. But since the diversity loss is only optimized on mini-batches of a finite dataset and the weight should be set to a small value (otherwise it will seriously reduce the quality), the degree of diversity is limited. By contrast, the different orthogonal noise matrices can be innumerable and diverse, so there could be endless possibilities with distinct diversity for the results of our approach. Moreover, our method is learning-free and can be effective for arbitrary styles, while the diversity loss of~\cite{li2017diversified,ulyanov2017improved} needs to be optimized every time for every style.

\renewcommand\arraystretch{0.5}
\begin{figure}[t]
	\centering
	\setlength{\tabcolsep}{0.03cm}
	\begin{tabular}{cccccc}
		\includegraphics[width=0.16\linewidth]{figs/5/0_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/3_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/5_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/6_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/8_1.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/10_1.jpg}\\

		\includegraphics[width=0.16\linewidth]{figs/5/0_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/3_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/5_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/6_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/8_2.jpg}&
		\includegraphics[width=0.16\linewidth]{figs/5/10_2.jpg}\\

		\scriptsize $\lambda=0$&\scriptsize $\lambda=0.3$&\scriptsize $\lambda=0.5$&\scriptsize $\lambda=0.6$&\scriptsize $\lambda=0.8$&\scriptsize $\lambda=1.0$\\
	\end{tabular}
	\caption{Trade-off between diversity and quality by varying diversity hyperparameter $\lambda$ in method~\cite{sheng2018avatar} (+ our DFP).
	}
	\label{fig:fig5}
\end{figure}
\renewcommand\arraystretch{0.5}
\begin{figure}[t]
	\centering
	\setlength{\tabcolsep}{0.03cm}
	\begin{tabular}{ccccc}
		\includegraphics[width=0.194\linewidth]{figs/pw/0_1.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/4_1.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/6_1.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/8_1.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/10_1.jpg}\\

		\includegraphics[width=0.194\linewidth]{figs/pw/0_2.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/4_2.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/6_2.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/8_2.jpg}&
		\includegraphics[width=0.194\linewidth]{figs/pw/10_2.jpg}\\

		\scriptsize $\lambda=0$&\scriptsize $\lambda=0.4$&\scriptsize $\lambda=0.6$&\scriptsize $\lambda=0.8$&\scriptsize $\lambda=1.0$\\
	\end{tabular}
	\caption{Trade-off between diversity and quality by varying diversity hyperparameter $\lambda$ in method~\cite{li2018closed} (+ our DFP).
	}
	\label{fig:pw}
\end{figure}
\section{Experimental Results}
\label{exp}
\subsection{Implementation Details}
\label{impd}
We incorporate our deep feature perturbation into three existing WCT-based methods which are used for different style transfer tasks, \ie, \cite{li2017universal} for artistic style transfer, \cite{sheng2018avatar} for semantic-level style transfer and \cite{li2018closed} for photo-realistic style transfer. Except for replacing the WCTs with our PWCTs, we do not modify anything else, such as pre-trained models, pre-processing or post-processing operations, etc. If not specifically stated, in all experiments, the stylization weight $\alpha$ of our diversified version is consistent with the original version, and the random noise matrix $N$ is sampled from the standard normal distribution. We fine-tune the diversity hyperparameter $\lambda$ to make our quality similar to previous works, \ie, 0.6 for~\cite{li2017universal}, 0.5 for~\cite{sheng2018avatar} and 1 for~\cite{li2018closed}. We will discuss these settings in the following sections. Our code is available at: \url{https://github.com/EndyWon/Deep-Feature-Perturbation}.

\begin{figure*}[htbp]
	\centering
	\includegraphics[width=1\linewidth]{figs/ss.png}
	\caption{Relation between diversity and stylization strength. Each column (except for the first one) shows the results obtained by different $\alpha$ values (stylization strength). The top row shows the results of the original method~\cite{li2017universal}. The middle row shows the results obtained by setting $\lambda=0.6$ (the default diversity strength) for our diversified version of~\cite{li2017universal}. The bottom row shows the results obtained by increasing the value of $\lambda$ to $1$ for our diversified version of~\cite{li2017universal}. $\alpha=0.6$ is the default stylization setting of~\cite{li2017universal}.
	}
	\label{fig:ss}
\end{figure*}
\renewcommand\arraystretch{0.5}
\begin{figure}[t]
	\centering
	\setlength{\tabcolsep}{0.03cm}
	\begin{tabular}{ccccc}
		\includegraphics[width=0.193\linewidth]{figs/6/c.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/wct1.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/sheng1.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/form1.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/wct_uniform.jpg}
		\\

		\includegraphics[width=0.193\linewidth]{figs/6/s.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/wct2.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/sheng2.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/form2.jpg}&
		\includegraphics[width=0.193\linewidth]{figs/6/sheng_mean_std.jpg}
		\\

		\scriptsize Inputs&\scriptsize Li~\etal~\cite{li2017universal}&\scriptsize Sheng~\etal~\cite{sheng2018avatar}&\scriptsize Li~\etal~\cite{li2018closed} &\scriptsize Varied Sampling\\
	\end{tabular}
	\caption{Relation between orthogonal noise matrix and generated result. The first column shows the input content (top) and style (bottom) images. The second to fourth columns show the results obtained by using the orthogonal noise matrix (top) and original random noise matrix (bottom) to perturb the methods~\cite{li2017universal,sheng2018avatar,li2018closed}, respectively. The last column shows the results obtained by varying the sampling distribution of the orthogonal noise matrix for methods~\cite{li2017universal} (top) and~\cite{sheng2018avatar} (bottom).
	}
	\label{fig:fig6}
\end{figure}
\subsection{Ablation Study}
\label{abstudy}
{\bf Single-level Perturbation versus Multi-level Perturbation.} We study the effects of single-level perturbation and multi-level perturbation on two WCT-based methods~\cite{li2017universal,li2018closed}, since they both use the multi-level stylization (while the method~\cite{sheng2018avatar} only uses a single-level stylization). To perturb only specific levels, we set the diversity hyperparameter $\lambda$ of the selected levels to default values (\ie, 0.6 for~\cite{li2017universal} and 1 for~\cite{li2018closed}), and the other levels to 0. As shown in the top row of Fig.~\ref{fig:fig2}, when we perturb separately from the deepest level (I5) to the shallowest level (I1), the quality decreases accordingly. This phenomenon exists in the top row of Fig.~\ref{fig:fig3} as well. We analyze the reason may be that the deeper level stylizes more low-frequency coarse characteristics while the shallower level stylizes more high-frequency fine characteristics, so adding noise into the shallower levels will affect the pixel performance of the final results. Perturbing at the deepest level can achieve comparable stylization quality as the original methods (see I5 in Fig.~\ref{fig:fig2} and I4 in Fig.~\ref{fig:fig3}). On the other hand, multi-level perturbation introduces noise into multiple levels, as shown in the bottom rows of Fig.~\ref{fig:fig2} and Fig.~\ref{fig:fig3}. We can see that introducing too much noise is unnecessary and will reduce the quality of stylization. We also compare the run-time in Table~\ref{tab1}. Note that for method~\cite{li2018closed}, we only consider the stylization time. Compared with the original methods (column 2), the incremental run-time decreases when we perturb the shallower levels. Nevertheless, the deepest-level perturbation only increases a very small amount of time (in {\bf bold}).

\renewcommand\arraystretch{0.7}
\begin{figure*}[h]
	\centering
	\setlength{\tabcolsep}{0.02cm}
	\begin{tabular}{cp{0.1cm}ccccccc}
		\includegraphics[width=0.12\linewidth]{figs/div/c.jpg}&&
		\includegraphics[width=0.12\linewidth]{figs/div/li/1.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/li/2.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/li/3.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/li/4.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/li/5.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/li/6.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/li/7.jpg}
		\\
		Content&&\multicolumn{7}{|c|}{Li~\etal~\cite{li2017diversified}}\\

		\includegraphics[width=0.12\linewidth]{figs/div/s.jpg}&&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/1.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/2.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/3.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/4.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/5.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/6.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/ulyanov/7.jpg}
		\\
		Style&&\multicolumn{7}{|c|}{Ulyanov~\etal~\cite{ulyanov2017improved}}\\

		\includegraphics[width=0.12\linewidth]{figs/div/wct.jpg}&&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/1.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/2.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/3.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/4.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/5.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/6.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/wctdfp/7.jpg}
		\\
		Li~\etal~\cite{li2017universal}&&\multicolumn{7}{|c|}{Li~\etal~\cite{li2017universal} + our DFP}\\

		\includegraphics[width=0.12\linewidth]{figs/div/avatarnet.jpg}&&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/1.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/2.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/3.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/4.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/5.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/6.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/avatardfp/7.jpg}
		\\
		Sheng~\etal~\cite{sheng2018avatar}&&\multicolumn{7}{|c|}{Sheng~\etal~\cite{sheng2018avatar} + our DFP}\\

		\includegraphics[width=0.12\linewidth]{figs/div/form.jpg}&&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/1.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/2.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/3.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/4.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/5.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/6.jpg}&
		\includegraphics[width=0.12\linewidth]{figs/div/formdfp/7.jpg}
		\\
		Li~\etal~\cite{li2018closed}&&\multicolumn{7}{|c|}{Li~\etal~\cite{li2018closed} + our DFP}\\

	\end{tabular}

	\caption{Qualitative comparisons of different diversified style transfer methods. The first column (from top to bottom) shows inputs and original outputs of~\cite{li2017universal,sheng2018avatar,li2018closed}. The other columns (from top to bottom) show diverse outputs of~\cite{li2017diversified,ulyanov2017improved} and~\cite{li2017universal,sheng2018avatar,li2018closed} (+ our DFP).
	}
	\label{fig:fig7}
\end{figure*}
{\bf Trade-off between Diversity and Quality.} In Eq.~(\ref{eq8}), we introduce a diversity hyperparameter $\lambda$ to provide user controls on the trade-off between diversity and quality. Different methods may require different $\lambda$ values. In this part, we demonstrate the impact of different $\lambda$ values on methods~\cite{li2017universal,sheng2018avatar,li2018closed} while keeping their default stylization settings. For method~\cite{li2017universal} and~\cite{li2018closed}, we only perturb the deepest level as suggested in the former sections. For method~\cite{sheng2018avatar}, we perturb its bottleneck layer as it only uses a single-level stylization. The results are shown in Fig.~\ref{fig:fig4},~\ref{fig:fig5} and~\ref{fig:pw}. As we can see, the degree of diversity rises with the increase of $\lambda$ values, but for method~\cite{li2017universal} and~\cite{sheng2018avatar} (Fig.~\ref{fig:fig4} and~\ref{fig:fig5}), the quality is obviously reduced when large $\lambda$ values are applied. However, this problem does not arise in method~\cite{li2018closed} (Fig.~\ref{fig:pw}), it may be because this method~\cite{li2018closed} contains a smoothing step to remove noticeable artifacts and it suppresses the emergence of diversity to some extent, which will also be verified by the quantitative comparisons in later Section~\ref{com}. For trade-offs, we finally adopt 0.6, 0.5 and 1 for the default $\lambda$ values of~\cite{li2017universal},~\cite{sheng2018avatar} and~\cite{li2018closed}, respectively.

{\bf Relation between Diversity and Stylization Strength.} The diversity is also related to the stylization strength. Taking method~\cite{li2017universal} as an example, Fig.~\ref{fig:ss} demonstrates the relation between these two aspects. Comparing the top two rows, we can observe that for our default diversity setting ($\lambda=0.6$), it works well for the situations where the stylization strength $\alpha\leq 0.6$, but destroys the content structure for those with larger $\alpha$ values. We set a larger diversity strength ($\lambda=1$) in the bottom row, and we can observe that it still works fine for those with lower stylization strength (\eg, $\alpha\leq 0.4$). That is to say, we can set a larger diversity strength for a smaller stylization strength. In fact, as we have analyzed in Section~\ref{DFP}, our diversity may affect the content information from $\hat{F_c}$ (Eq. (\ref{eq5})), so the content structure will be overwhelmed by the style patterns when the value of $\lambda$ is too high, as validated in the last two columns. Therefore, the tradeoff between stylization strength ($\alpha$) and diversity strength ($\lambda$) should be considered. Nevertheless, in practice, users only need to first determine the optimal stylization strength $\alpha$ (usually the default one) for different methods, and then adjust the appropriate $\lambda$ values to keep the quality. Besides, in each method, our results have verified that the constant $\lambda$ value can work {\em stably} on different content and style inputs.

{\bf Locations to Insert the Orthogonal Noise Matrix.} In Section~\ref{DFP}, we have mentioned three places to insert the orthogonal noise matrix in Eq.~(\ref{eq6}), \ie, between $D_s^{\frac{1}{2}}$ and $E_s^T$, between $E_s^T$ and $\hat{F_c}$, and on the right side of $\hat{F_c}$. We conduct the same experiments for each of them and find that there is no difference in qualitative comparisons. But in quantitative comparisons, \eg, run-time and computation requirements, there are some differences. This is mainly due to the different computation of matrix multiplication caused by the different size of noise matrix. As we have analyzed in Section~\ref{DFP}, when we insert the orthogonal noise matrix ${\bf Z}$ between $D_s^{\frac{1}{2}}$ and $E_s^T$, the size of ${\bf Z}$ is only $(C-k)\times (C-k)$, where $C$ is the number of channels and $k$ is the number of small singular values in $D_s^{\frac{1}{2}}$. For the other two cases, since the shapes of $E_s^T$ and $\hat{F_c}$ are $(C-k)\times C$ and $C\times H_cW_c$, respectively (where $H_c$, $W_c$ are the height and width of the content feature), the size of ${\bf Z}$ should be $C\times C$ if we insert it between $E_s^T$ and $\hat{F_c}$, and $H_cW_c\times H_cW_c$ if we insert it on the right side of $\hat{F_c}$. Generally, for the deepest level, $C-k<C<H_cW_c$, so we eventually insert ${\bf Z}$ between $D_s^{\frac{1}{2}}$ and $E_s^T$ as this may consume the least computation and run-time.

\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}
\begin{table}[h]
	\caption{Quantitative comparisons of different methods. We measure diversity using average Pixel distance and LPIPS distance~\cite{zhang2018unreasonable}.}
	\centering
	\hspace{1.5cm}
	\begin{tabular}{ccc}
		\toprule
		Method&\tabincell{c}{Pixel \\Distance}&\tabincell{c}{LPIPS \\Distance}\\
		\midrule
		Li \etal~\cite{li2017diversified}&0.080&0.175\\
		Ulyanov \etal~\cite{ulyanov2017improved}&0.077&0.163\\
		Li \etal~\cite{li2017universal}&0.000&0.000\\
		Sheng \etal~\cite{sheng2018avatar}&0.000&0.000\\
		Li \etal~\cite{li2018closed}&0.000&0.000\\
		\midrule
		Li \etal~\cite{li2017universal} + our DFP&{\bf 0.162}&{\bf 0.431}\\
		Sheng \etal~\cite{sheng2018avatar} + our DFP&{\bf 0.102}&{\bf 0.264}\\
		Li \etal~\cite{li2018closed} + our DFP&{\bf 0.091}&{\bf 0.203}\\
		\bottomrule
	\end{tabular}
	\label{tab2}
\end{table}
{\bf Relation between Orthogonal Noise Matrix and Generated Result.} To verify the importance and necessity of the orthogonal noise matrix ${\bf Z}$ in our DFP, we compare it with the original random noise matrix $N$, and also discuss the influence of its sampling distribution. The results are shown in Fig.~\ref{fig:fig6}, as we can see, using the original random noise matrix produces low quality results (see column 2 to 4 in bottom row). The results obtained by~\cite{li2017universal} and~\cite{sheng2018avatar} are just like combinations of texture and noise, which drown out the content information. Compared with the former two,~\cite{li2018closed} can maintain the content information as much as possible even with the original random noise perturbation. This may be because it consists of two steps, and the second step removes noticeable artifacts to maintain the structure of the content image. But as the result shows, the quality is still significantly reduced. Similar to the former experiments, we also adjust the values of $\alpha$ and $\lambda$ for original random noise perturbation, but the poor generation effect still cannot be alleviated. To explore the influence of sampling distribution of orthogonal noise matrix, we use uniform distribution instead of the standard normal distribution for method~\cite{li2017universal} (see the last column in top row), and vary the mean and standard deviation of normal distribution for method~\cite{sheng2018avatar} (see the last column in bottom row). As we can see, the generated images do not show a significant difference from the default ones, which indicates that the key factor affecting the result is the orthogonality of noise ${\bf Z}$, rather than its sampling distribution.

\subsection{Comparisons}
\label{com}
In this section, we incorporate our DFP into methods~\cite{li2017universal,sheng2018avatar,li2018closed} and compare them with other diversified style transfer methods~\cite{li2017diversified,ulyanov2017improved} from both qualitative and quantitative aspects. For methods~\cite{li2017diversified} and~\cite{ulyanov2017improved}, we run the author-released codes or pre-trained models with the default configurations. For our methods, we use the default settings as described in Section~\ref{impd}.

{\bf Qualitative Comparisons.} We show qualitative comparison results in Fig.~\ref{fig:fig7}. We observe that~\cite{li2017diversified} and~\cite{ulyanov2017improved} only produce subtle diversity (\eg, slight changes in the faces), which does not contain any meaningful variation. By contrast, for the methods with our DFP, the results show a distinct diversity (\eg, the faces, the hairs, the backgrounds, and even the eyes). Compared with the original outputs, the results obtained by incorporating our DFP are almost without quality degradation.

{\bf Quantitative Comparisons.} We compute the average distance of sample pairs in pixel space and deep feature space to measure the diversity, respectively. For each method, we use 6 content images and 6 style images to get 36 different combinations, and for each combination, we obtain 20 outputs. There are totally 6840 pairs (each pair has the same content and style) of outputs generated by each method, we compute the average distance between them.

In pixel space, we directly compute the average pixel distance in RGB channels, which can be formulated as follows:
\begin{equation}
d_{pixel} (\vec{x}_1, \vec{x}_2)= \frac{||\vec{x}_1-\vec{x}_2||_1}{W\times H\times 255\times 3},
\label{pd}
\end{equation}
where $\vec{x}_1$ and $\vec{x}_2$ denote the two images to compute the pixel distance. $W$ and $H$ are their width and height (they should have the same resolution).

In deep feature space, we use the LPIPS (Learned Perceptual Image Patch Similarity) metric proposed by Zhang~\etal~\cite{zhang2018unreasonable}. It computes distance in AlexNet~\cite{krizhevsky2014one} feature space ($conv1\_5$, pre-trained on Imagenet~\cite{russakovsky2015imagenet}), with linear weights to better match human perceptual judgments.

As shown in Table~\ref{tab2}, \cite{li2017diversified} and~\cite{ulyanov2017improved} produce low diversity scores in both Pixel and LPIPS distance. Without our DFP, the original methods~\cite{li2017universal,sheng2018avatar,li2018closed} cannot generate diverse results. By incorporating DFP, these methods show great diversity improvement. Note that since the method~\cite{sheng2018avatar} (+ our DFP) is still restricted by some semantic constraints when transferring styles, and method~\cite{li2018closed} (+ our DFP) contains a smoothing step to remove detailed effects, their diversity scores are lower than those of method~\cite{li2017universal} (+ our DFP).

\section{Conclusion}
In this work, we introduce deep feature perturbation (DFP) into the whitening and coloring transform (WCT) to achieve diversified arbitrary style transfer. By incorporating our method, many existing WCT-based methods can be empowered to generate diverse results. Experimental results demonstrate that our approach can greatly increase the diversity while maintaining the quality of stylization. At this stage, we only explore the WCT-based methods, but this learning-free and universal paradigm may inspire a series of more ingenious and effective works in the future. Besides, WCT has also been widely used in many other fields, such as {\em image-to-image translation}~\cite{cho2019image}, {\em GANs}~\cite{siarohin2018whitening}, etc. Therefore, we believe our method may also provide a good inspiration for these research fields.

{\bf Acknowledgments.} We sincerely thank the anonymous reviewers for helping us to improve this paper. This work was supported in part by the Zhejiang science and technology program (No: 2019C03137), and Zhejiang Fund Project (No: LGF18F020006, LY19F020049).

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Q: Как найти дубликат изображения из других изображений? У меня есть входное изображение и я должен найти похожие в папке с кучей картинок.
Я попробовал сделать через PIL.ImageChops.difference но мои изображения маленькие (50x50) и он считает их слишком похожими. Примеры -

(предметы из игры the binding of isaac) я хочу принимать скриншот из игры с предметом и находить его в базе чтобы выдать нужную информацию. Сам предмет выглядит всегда одинаково, но фон может меняться.

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text:
Parasyntormon lepus är en tvåvingeart som beskrevs av Van Duzee 1918. Parasyntormon lepus ingår i släktet Parasyntormon och familjen styltflugor.

Artens utbredningsområde är Kalifornien. Inga underarter finns listade i Catalogue of Life.

Källor

Styltflugor
lepus

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text:
by Judith Okongo November 18, 2019 098
The adjudication of the awards is on-going and the finalist for the various awards with announced early next week. The adjudication is conducted by a five-person regional jury made up of industry experts from Kenya, Uganda, Tanzania, Rwanda and South Africa.
By CW Correspondent
More than 60 percent of the companies that entered into the most coveted IT excellence awards in East Africa have been shortlisted with Kenya being ahead of the pack.
150 out of 384 companies were shortlisted successfully ahead of the major IT symposium and Awards that will be held in Naivasha from 21 November, 2019.
This award covers six East African countries including Kenya, Uganda, Tanzania, Rwanda, Burundi and Ethiopia. "This year for the first time, we saw companies from Ethiopia making submissions to be considered for the award," said Andrew Karanja, Director, CIO East Africa. There has been a steady uptake of technology in Ethiopia as the market continues to open up.
The CIO100 Awards recognizes companies that have used IT in the most innovative way to solve business challenges and achieve organizational goals. The awards have been running for the last none years and have become a yardstick to benchmark a company's innovativeness among its competitors and industries.
Some of the companies that made submissions from Ethiopia include Gebeya, a technical training company based in Addis Ababa, i-Tech Systems Engineering and Consulting, Meheda Food Processing Company, East Africa Tiger Brands, Development Bank of Ethiopia, Zemen Bank and Dashen Bank SC.
The banking and financial services sector makes up almost 40% of the submissions including from both Commercial Bank of Africa and NIC Bank, now rebranded NCBA after the merger, Equity Bank, DTB Bank, Standbic Uganda, Standard Chartered Bank Kenya, I&M Bank, Sidian Bank and SMB Bank to name just a few.
Insurance and the savings and credit sectors are also very well represented with most of the large insurance companies making submissions.
These include UAP Old Mutual, the Madison Group, AAR Insurance, Bluewave Insurance, Mwalimu Sacco, Bandari Sacco, Chai Sacco and Boresha Sacco just as a sample of the submissions.
Manufacturing and hospitality industries had the lowest entries this year with both recording less than 2% of the entries. "Manufacturing and hospitality industries numbers went down this year compared to the last two years where we had a strong field from those two industries," said Melissa Dorsilla, the CIO100 Project Lead at CIO East Africa.
Government agencies and ministries also made substantive submissions these include Kenya Revenue Authority, Uganda Revenue Authority, Kenya Electricity Transmission Company (KETRACO), Uganda Investment Authority, National Drug Authority – Uganda, National Hospital Insurance Fund among others.
The adjudication of the awards is on-going and the finalist for the various awards with announced early next week.
The adjudication is conducted by a five-person regional jury made up of industry experts from Kenya, Uganda, Tanzania, Rwanda and South Africa.
The jury is led by Prof Louis Fourie, former VC at the Western Cape Peninsula University of Technology.
World Photography Awards chooses Kenya's best photograph
Catherine Muema April 26, 2018 April 28, 2018
Kenyan Varsity Student Hostels to Be Built and Maintained on a PPP
Catherine Muema February 21, 2018
Second Edition Of UNFPA Business Competition Winner to get Sh3 Million Grant
Ker Mogallo September 28, 2018 September 28, 2018

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We're 70 years post-independence and we still consider the opinions of white people to be more important than ours.
This summer, a white American male comedian, Jeremy McLellan, came to Pakistan to do a bit of social work. He worked with a team that provided free dental care to kids in villages. Since he is a comedian, he planned a few comedy shows in the country as well. His schedule only allowed for him to perform in two cities; Lahore and Islamabad.
Jeremy posted about his impending visit on Facebook constantly, and his predominant Asian Muslim online community was very excited.
People were counting down the days leading up to his visit.
A white person excited to go to Pakistan, my, how the tables have turned, eh?
His shows for Lahore and Islamabad were immediately sold out and people could not have been more excited to have Jeremy in Pakistan. He chronicled his entire trip on Facebook with a post every night about the day's adventures.
In one of the posts, Jeremy mentioned that he saw remnants of colonialism were still strong in Pakistani society today. He noticed how he was treated better than those around him.
When people commented that this was because Pakistanis as a people are incredibly welcoming and treat guests with respect, Jeremy clarified that this was not the case since his American friend of Pakistani origin was not treated with the same respect Jeremy was, even though technically his friend was a guest as well.
Even Jeremy noticed the colonial mindset that is still entrenched in Pakistani minds. Jeremy really hit the nail on the head on that one: Pakistani society is still deeply entrenched in the colonial mindset.
It is obvious in our obsession with fair skin and speaking English.
So when a popular white person came to Pakistan, we felt we had to please them, consciously or unconsciously, because we think that they are superior to us and their approval is the one that counts.
When Jeremy pointed out that the colonial mindset that still existed, people agreed.
But something that has been absolutely nagging at me is how we constantly celebrate and revel in how much Jeremy loves Pakistan. Don't get me wrong, I smile whenever I see his positive posts about Pakistan. Why wouldn't I? I love my country. But what irks me is how important we have started to hold his opinion.
I am not attacking Jeremy specifically. We do this with every white person.
I have seen it too many times.
The only difference is Jeremy made public posts that we share and these posts make us feel validated.
Do we really need a white man to tell us our country is great in order for us to love it? No.
I went to Jeremy's show. He seemed like a great guy and his love for Pakistan seemed genuine. But constantly needing his validation on everything within Pakistan is not something I am a fan of.
Yes, it is great that a tourist came to Pakistan and had a great time.
It is even better that his views on the country provided a counter-narrative to the widespread belief in the West that Pakistan is a war zone. He showed that Pakistan is just a normal country and Pakistanis are like the citizens of any other country.
But we should not take his opinions so personally or believe in them so strongly – just because of the color of his skin.
It is time we grew out of this colonial mindset.
We are a free country and aren't ruled by white people anymore. We should stop feeling the need to please them.
A white person's seal of approval should not be what prompts you to be proud of your country. The question is when that'll actually change.
Can I be feminist and Catholic as a Filipina?
I didn't see a point to therapy. Then my life spiraled out of control.


Scotland's voice for spina bifida, hydrocephalus and related conditions.
I was chatting to a fellow Celtic fan about the joys of getting out and about meeting up with other Celtic fans over the festive period. He was telling me that if was difficult for him to attend many of these gatherings because of family commitments and having a child who suffers from a form of Spina Bifida. It was one of those conversations that help put things in to perspective.
As you know a key element of TCN is to raise awareness and support for charities and good causes but as we have stated in the past along with our Partner charities we are happy to raise awareness of any good cause that individual Celtic fans bring to our attention. Therefore as our first ever Christmas charity spotlight we would like you to consider supporting the Scottish Spina Bifida Association.
Here is some information from the SSBA web site.
Spina Bifida is a fault in the spinal column in which one or more veterbrae (the bones which form the backbone) fail to form properly, leaving a gap or split, causing damage to the nervous system.
The central nervous system and spine develops between the 14th and 23rd day after conception. Spina bifida occurs when the neural tube fails to close correctly. The vertebrae also fail to close in complete rings around the affected portion of the spinal cord. This leaves a gap at the back, involving one or more vertebrae. The fault may occur in one or more of the vertebrae but it is most common around waist-level.
Hydrocephalus: Most babies born with Spina Bifida have Hydrocephalus. In addition to the lesion in the spinal cord, there are abnormalities in the structure of certain parts of the brain that develop before birth. This prevents proper drainage of the CSF. The increase in pressure due to this can also compress the abnormal parts of the brain even further.
So over the Holiday season we would like to ask that you visit the SSBA web site to see the wonderful work and support the association carry out to help sufferers and their families.
Thanks to Liam Robson @FreeGellyBhoy For brining the good work of SSBA to our attention. We wish Liam and his family a Merry Xmas and a happy and peaceful 2012.


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Diet Plans A-Z
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Managing High Potassium While on Dialysis
Are There Health Benefits to Using Clove Essential Oil?
Potential Health Benefits of Clove Essential Oil
Potential Risks of Clove Essential Oil
Essential oils have become immensely popular over the past decade. Clove essential oil is derived from the flower buds of the Eugenia caryophyllata tree, a member of the myrtle family. While originally native to just a few islands in Indonesia, cloves are now cultivated in several places around the world.
Clove essential oil has long been a popular remedy for tooth pain. Reports of it being used for this purpose date back more than 300 years. In China, it has been used for multiple purposes for more than 2,000 years, including as an antiparasitic agent.
Clove essential oil has become synonymous with health and wellness for some of its fans. However, there are serious health risks associated with the substance. Research can help you find the boundary between healthful and harmful.
There isn't readily available information on the nutritional make-up of clove essential oil. This is because clove essential oil isn't particularly safe to ingest. The substance eugenol, which is a major component of clove essential oil, can be toxic in high concentrations. If you want to flavor your food with cloves, it's advisable to use the dry spice.
A single teaspoon of ground cloves contains approximately:
Fat: 0.3 grams
Carbohydrates: 1.3 grams
Fiber: 0.7 grams
In terms of vitamins and minerals, cloves are high in:
The best internal benefit of clove essential oil has been apparent for many generations now — its fabled effectiveness for easing toothaches.
Treating Dental Pain
The use of clove oil on a toothache was first documented in 1649 in France. It continues to be a popular solution today, thanks to the powerful molecule, eugenol. Eugenol is a natural anesthetic.
While clove essential oil is good for treating pain, there is insufficient evidence that it also effectively kills the bacteria causing the problem.
Treating Dental Erosion
Some acidic foods and drinks may decalcify (break down) tooth enamel. Eugenol in clove oil, when used as a topical treatment, may reverse or minimize the effects of dental erosion, one study found.
However, more research is needed to fully explore the benefits of clove oil as a treatment or preventive ointment for tooth enamel erosion.
As mentioned, there are some serious health risks to ingesting clove essential oil. In high enough doses, it is toxic to humans.
Toxic Effects on the Liver
Case studies have been reported involving the ingestion of toxic amounts of clove oil (10-30 ml, or roughly 2-6 teaspoons). Symptoms include agitation, declining consciousness, and coma.
Symptoms emerged within hours of ingesting the essential oil. Signs of liver injury, including jaundice, arose between 12 and 24 hours after ingestion. Those most likely to experience liver injury are infants and young children.
Even when not ingested, eugenol has been known to cause allergic reactions during dental procedures. Dentists have used eugenol mixed with different materials to create impressions and fillings for more than a century.
In that time, eugenol has been observed to cause mild to severe allergy symptoms in some patients. Symptoms of an allergic reaction to dental eugenol have been reported to include irritation at the application site and raised welts across the body.
WebMD Medical Reference Reviewed by Dan Brennan, MD on October 28, 2020
Archives of Disease in Childhood: "Near Fatal Ingestion of Oil of Cloves."
BMD Case Reports: "An Unexpected Positive Hypersensitive Reaction to Eugenol."
International Journal of Dentistry: "In Vitro Inhibitory Effect of Clove Essential Oil and Its Two Active Principles on Tooth Decalcification by Apple Juice."
International Journal of Research in Pharmaceutical and Biomedical Sciences: "Eugenia caryophyllata Thunberg (Family Myrtaceae): A Review."
LiverTox: "Eugenol (Clove Oil)."
Molecules: "Eugenol – From the Remote Maluku Islands to the International Market Place: A Review of a Remarkable and Versatile Molecule."
The Open Dentistry Journal: "Iatrogenic Damage to the Periodontium by Chemicals and Dental Materials."
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The 1953 edition of the Campeonato Carioca kicked off on July 11, 1953 and ended on January 20, 1954. It was organized by FMF (Federação Metropolitana de Futebol, or Metropolitan Football Federation). Twelve teams participated. Flamengo won the title for the 11th time. no teams were relegated.

System
The tournament would be divided in two stages:
 First phase: The twelve teams all played against each other in a double round-robin format. The six best teams qualified to the Third round (The first and second rounds were part of the First phase). The team with the most points qualified to the Finals.
 Third round: The remaining six teams all played in a single round-robin format against each other. The team with the most points qualified to the Finals.
 Finals: The finals would be disputed in a best-of-three-points series. In case the same team won all three rounds, it won the title automatically.

Championship

First phase

Third round

Top Scores

References

Campeonato Carioca seasons
Carioca