text:
My Journey to 1K Status on United Airlines – Part 1 – Free Miles Traveler
Mileage Runs
Journey to 1K Status
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Journey to 1K Status, Latest
My Journey to 1K Status on United Airlines – Part 1
Written by Steve on February 9, 2013
More in Journey to 1K Status:
Mileage Run # 4: Weekend in Dubai April 26, 2013
Mileage Run #3 – Long Weekend in Istanbul Turkey March 26, 2013
My Journey to 1K Status on United Airlines – Part 2: First Dublin Run February 19, 2013
There comes a time in one's life when you need to make a change. For me and my family, that happened last summer after 20 plus years in the Windy City. We lived right in the city of Chicago in the coolest family neighborhood called Old Irving Park where we had made some great lifelong friends. All the while, I was an American Airlines Platinum with 2 million miles under my belt, cranking our miles with my job as a CFO for some public companies and private equity-backed portfolio companies in several industries. While we totally love Chicago and all it has to offer, we sensed it was time for a new adventure, especially after we had spent 5 weeks in Fort Collins CO the summer before. We knew how awesome living in Colorado would be. I have transitioned job-wise into an contract CFO and an internet entrepreneur over the last several years so I was able to make the move pretty easily.
Once we settled in Denver, I set about learning all I could about United Airlines, since Denver is one of their seven main hubs here in the US. While I love AA, I just did not want to have to always fly to Dallas or Chicago to catch another flight. Plus, as I read more and more, I learned that United has the most flexibility and breadth to its frequent flyer program and so the race was on. Once we got by the new year, I began to plot my journey to 1K status in the Mileage Plus program at United Airlines. To get 1K, you must fly 100,000 in-air miles on United or any of their partners. My first step was getting united to match my Platinum status with American Airline with the similar level at United, which is Premier Gold. It is usual for airlines to match each others statuses as long as the match is from a competing alliance (beware that airlines tend not to match from their alliance partners — i.e. United won't match status from Lufthansa as they are both in Star Alliance). United has long had a status match program and they have just updated the terms and conditions of this program.
That process was very smooth. I just mailed my request with a screengrab of my AA account balances to premiermatch@united.com. Once approved as a newly minted Premier Gold, I need to fly 18,000 miles within 90-days to retain their matched status for the rest of the year. Elite status is extremely valuable and generally when you request a match or challenge after July 1 of each year, that status is then good until February of the year after next. That means if you were to get matched/challenged on July 1, 2013, it would be good until February 2015. Airline status comes with valuable perks including priority check-in and boarding, a speedier security line, bonus miles and, in the case of airlines with first-class service, upgrades to that cushy, curtained-off cabin. In short, it makes for a less-stressful trip. Achieving the top-tier 1K status with United gives me lots of great perks, including these not available at the Gold or Platinum level:
Twenty-five percent more bonus miles for each flight taken during 2013.
Higher priority for complimentary upgrades, wait lists and standby clearance.
Instant upgrades to first class on mid-priced coach tickets, when flying domestically.
Access to a dedicated call center line staffed by U.S.-based agents.
A $60 statement credit on my United MileagePlus Chase Visa credit card.
Six Global Premier Upgrades for use on international flights.
In this journey, I will document every trip, every mile gained, and every location I visit on my way to getting the illustrious 1K status. Join me as I take to the skies!
MILEAGE RUN STRATEGY
But first, before I go, I must discuss the two separate and distinct strategies people employ to get miles. Either you decide you want to sleep in your own bed so long days and lots of domestic miles are on tap for you or you decide to do your mileage runs on the weekends, thereby giving you access to longer international mileage runs while not infringing too much on your day job. Right now, there are $218 transcon flights from SFO-EWR, giving you nearly 10K in miles and back home that night in your bed. For me, the low cost of the long international flights along with the opportunity to see some faraway countries for 24-36 hours have made it easier for me to choose weekend mileage runs.
HOME WORK TO DO
My first step was to learn as much about mileage runs as I could at Flyertalk.com, the online frequent flyer community and mileage-runner Mecca, then use the first quarter of 2-013 to take advantage of the low fares in the winter months to knock out some mileage runs. What is a Mileage Run? In its simplest terms, it is a trip for the purpose of accumulating frequent flyer miles and elite status. Some people even combine Mileage Runs with vacations, business trips, and ordinary travel to increase their mileage balances or reach the next elite level. According to Flyertalk.com, "A true mileage run is a trip that's sole purpose is to earn miles or points in a particular travel industry loyalty program." That definition barely scratches the surface of a practice that is also a celebration of the mechanics of traveling, a sort of video game turned reality, and the opportunity to live the high life without paying the price. In order to be a mileage runner, you must embrace the attributes of modern travel that most people despise. Interestingly, this doesn't seem to be a problem for most mileage runners, but rather an incentive. Randy Petersen, the founder of FlyerTalk.com and the editor of InsideFlyer magazine, estimates that about 50,000 passengers take off on what he calls "mileage runs" from mid-November to the end of the year. Most of them, according to Mr. Petersen, have the same agenda: "Don't spend much time or money." After all, it's not a free perk if you have to work hard or pay much to get it.
TOOLS TO USE
Kayak has a great interactive map called KAYAK EXPLORE (see link below). You put in your starting city and your budget. It then populates all of the cities that you can travel to with the prices. It isn't always perfect, because it just aggregates recent searches, so sometimes those deals are gone by the time you search for it. But for the most part, I have found the information pretty accurate. Try it, and I promise you will be off in travel dreamland. Right now, since I am searching DEN as a starting point, I am able to see that there are $640 RTs to Moscow (SVO), $660 to Oslo (OSL), $670 to Stockholm (ARN), $670 to Copenhagen (CPH) and $710 to Dublin (DUB), among others. There are also great filters to help you find your cheap dream place. Want to go somewhere warm? Eliminate all locations with an average daily temp below 75 degrees. Want to ski? Click on the ski box.
However, like most of the mileage runners, Matrix.itasoftware.com is my secret weapon when it comes to airfare searches. This site is great when my travel plans are pretty firm and I need to find the best flight combinations. With over 30 million scheduled commercial flights per year (about one every second), finding the right flight at the right price can be a daunting task. The big online travel agencies try to present options in a simple format, but sometimes you need to dig a little deeper to get the best deal. The power of ITA's Matrix tool lies in its ability to specify multiple origin and destination airports in one query. For example, I often try to do long MRs out of DEN when I can. Matrix lets me search for all flights leaving from DEN going to Seattle, San Francisco, Portland, San Diego or the other coast, airport in a single search. This would require nine separate searches on any other site, but only a single inquiry with ITA's Matrix. Add in the ability to be flexible around dates, compare non-stop vs. connecting flights, and multi-city itineraries, and you can easily see how this is by far the most power tool out there for airfare searches. It's no wonder that Google acquired ITA last year to power their new Google Flights feature.
MY FIRST MILEAGE RUN – Denver to Dublin, Ireland
Soon I will start my first mileage run from Denver to Dublin, Ireland. Like many, to get psyched up as a mileage runner will require many cups of java at the home coffeepot and fill ups along the way, steeling myself for a grueling day in the air. For me, the first mileage run on United will be Denver to Los Angeles, enjoying one hour of bliss then off to a transcontinental flight to Washington DC, where it is dinner at the airport before the overnight flight to Dublin, arriving there Saturday morning. I'll be able to take in about 24 hours of sights and sounds of Dublin, before turning around Sunday morning and heading back from Dublin to Newark, NJ, then another trancontinental flight to Los Angeles and then back to Denver and my own bed Sunday night. What does this craziness get me? I will earn 13,063 miles on this mileage run to Dublin, on my way to 1K status on United Airlines.
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The feet and ankles are a very important component of the body's balance and proprioceptive systems. Your vertical body weight needs to be translated onto a horizontal surface, and it needs to be distributed efficiently throughout the network of load bearing joints. The average human takes 10 000 steps per day, and if the system is faulty, then this will be compounded greatly over time.
A simple test can be to look at someone's feet and ankles from the rear. Do their ankles follow a straight line through their leg bone? Does one or both of them buckle inwards or outwards? A deviation of this type can indicate that the supportive structures are not distributing their weight correctly through the joint network.
Another simple test is to watch someone standing on one leg. Do they hold their hips level or does one sag down? Once again, this gives the chiropractor valuable information regarding how the weight distribution is working.
At Walkerville Chiropractic, we take a whole body approach to problems that are more complicated, and work with you by giving you advice on self-care, stretching and exercises that can give you the maximum benefit.
Valente G, Taddei F, Jonkers I. Influence of weak hip abductor muscles on joint contact forces during normal walking: probabilistic modeling analysis. J Biomech. 2013 Sep 3;46(13):2186-93.


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The Provincial and Territorial Culture Indicators (PTCI) are timely economic estimates of culture and sport in Canada. The PTCI is derived directly from the Canadian Culture Satellite Account (CSA), an accounting framework developed to measure the economic importance of culture, the arts, heritage and sport to the Canadian economy.
Culture GDP in Canada totalled $53.8 billion in 2016, a 1.5% increase from the previous year. Culture accounted for 2.8% of Canada's overall GDP. The importance of culture varied considerably across provinces and territories, ranging from a share of 1.3% of GDP in Saskatchewan to 3.5% in Ontario. Culture jobs in Canada were up 2.2% to 652,406, while economy-wide jobs grew 1.1% in 2016. Culture jobs accounted for 3.5% of all jobs in the economy. Regionally, culture jobs as a share of total economy jobs ranged from 1.8% in Nunavut to 4.0% in British Columbia.
The Cultural Satellite Account was developed by Statistics Canada with the support of a diverse group of partners referred to as the Culture Statistics Strategy Consortium which includes the Canada Council of the Arts.
Image of a map of Canada with lines connecting each province/territory to individual boxes. Boxes contain the name of the province/territory and the statistical information relating to the specific jurisdiction it points to.


We often think of computers as big number crunchers. Although they do that, reality computing is where computers play a role in interacting with the real world. This can involve getting the real world into the computer via methods like scanning or photogrammetry, or it can involve getting data out of the computer into the real world via processes like CNC or 3D printing.
I stumbled across this today. It is a fun read.
Reality is alive in the lab.


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General News · 21st January 2019
You are invited to the monthly All-Island Book Club
Manda Aufochs Gillespie
All ages are welcome to join us to discuss the following books at the all-island book clubs. These meet on Tuesdays at 1 p.m. at Linnaea on the following dates.
January all-island book club date: 1/22
Book: Are You There God? It's Me Margaret. by Judy Blume fiction, coming-of-age
February (Black history month) book club date : 2/19
Book: Chains by Laurie Halse Anderson (optional continue trilogy) historic fiction
March All-island book club date : 4/2
Book: Golden Compass by Philip Pullman (optional: continue trilogy of His Dark Materials
April book club date: 4/23
Book: Hope for Wildlife by Ray MacLeod non-fiction, essays
May book club date: 5/21
Book: Tuck Everlasting by Natalie Babbit
More about the books
About Are you there God,?It's Me Margaret.:
Margaret Simon, almost twelve, likes long hair, tuna fish, the smell of rain, and things that are pink. She's just moved from New York City to Farbook, New Jersey, and is anxious to fit in with her new friends—Nancy, Gretchen, and Janie. When they form a secret club to talk about private subjects like boys, bras, and getting their first periods, Margaret is happy to belong. But none of them can believe Margaret doesn't have religion, and that she isn't going to the Y or the Jewish Community Center. What they don't know is Margaret has her own very special relationship with God. She can talk to God about everything—family, friends, even Moose Freed, her secret crush. Margaret is funny and real, and her thoughts and feelings are oh-so-relatable—you'll feel like she's talking right to you, sharing her secrets with a friend. This is a timely book that deals with coming-of-age and puberty frankly and in a way that can get parents talking with their children.
About Chains...
Chains brings readers deeply and immediately—and emotionally—into events from almost two and a half centuries ago. A real wonder of voice and detail. The American Revolution is the backdrop, but what makes this different is that the story is told in sparkling detail by Isabel, a slave living in Boston, while the forces that would lead to war boil all around her. Chains is joined by Forge and Ashes in Anderson's "Seeds of America" trilogy.
Golden Compass by Philip Pullman (optional: continue trilogy of His Dark Materials) fantasy
Lyra is rushing to the cold, far North, where witch clans and armored bears rule. North, where the Gobblers take the children they steal-including her friend Roger. North, where her fearsome uncle Asriel is trying to build a bridge to a parallel world. Can one small girl make a difference in such great and terrible endeavors? This is Lyra: a savage, a schemer, a liar, and as fierce and true a champion as Roger or Asriel could want-but what Lyra doesn't know is that to help one of them will be to betray the other. Philip Pullman is most famous for his His Dark Materials trilogy, a series of young adult fantasy novels which feature free-thought themes.
Hope for Wildlife by Ray MacLeod non-fiction, essays
Since 1997, thousands of animals have been saved through the tireless efforts of the staff and volunteers at Hope for Wildlife, a Nova Scotia wildlife rehabilitation centre. Some animals stories were so unique that they garnered national attention such as founder Hope Swinimer s battle with the department of natural resources over Gretel, a member of the endangered pine marten species. Each creature comes with its own challenges, either through a difficult injury or a quirky personality like Lucifer the inexplicably bald and ornery raccoon but each pa- tient leaves an indelible mark on the lives of those around them. Hope for Wildlife tells the stories of fourteen different wild animals that have passed through the centre.
Tuck Everlasting By Natalie Babbit
This well-known story is about a girl, Winnie, who discovers a family of immortal folks, the Tucks, living a simple eternal life deep in the woods behind her house. There is a stranger who wants to use their gift for his own gain. Winnie is drawn into the beauty of the family's life, but also its dangers and the particular sorrow of everlasting life, while the world and its people age and pass by. Perfectly structured, this book is modelled like a giant wheel, turning page by page through the seasons. No reader will remain unchanged after reading it!
email Manda Aufochs Gillespie

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HOLDREGE- A pair of 1-5 teams faced off on Friday night, and it was the home team Holdrege winning against Lexington 28-20.
Lexington started the game off with a two-yard touchdown run from Jake Leger and the score was 6-0 in the first quarter. Holdrege then countered with a two-yard run of their own from Alex Wells in the second quarter and the score was then 8-6. The teams traded scores and at half Holdrege led 14-12.
In the second half, the battle continued, but Holdrege was able to extend their lead to 16 at one point as the score was 28-12 heading into the fourth quarter. Lexington tried to comeback, and they got within eight on a Leger one-yard run in the fourth quarter. On the ensuing possession, Holdrege had to punt with a minute left. Lexington tried to drive down the field, but was unable to score as the following play resulted in an interception for Holdrege's Tyler Buck.
For Lexington, Jake Leger had 192 total yards and three total touchdowns. Holdrege's Alex Wells had 221 yards on 45 carries and two touchdowns.
Holdrege is now 2-5 and will face McCook, for Lexington they are 1-6 and will play Hastings next week.


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Paris: Neymar was charged by UEFA on Friday for making "insulting" comments about the match officials following Paris Saint-Germain's shock Champions League loss to Manchester United.
UEFA said he had been charged, with the date of his hearing yet to be confirmed. Neymar could face a ban from the early stages of next season's Champions League.
"It's a disgrace. They get four guys who don't understand football to watch a slow-motion replay in front of the TV," Neymar wrote in Portuguese on Instagram after the game.


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I'm not a poser, I just fake a lot
Home » Interesting » Featured Posts » I'm not a poser, I just fake a lot
James Bachici
September 13, 2011 / 4 minutes read
It's high time we take a light-hearted look at the world of posers, who hope to impress but usually end up disappointing on so many levels.
How many of you have been walking or driving along, minding your own business when all of a sudden, your car radar goes off and your "spider sense" tingles? You know, that feeling that something special might be close? It's quite interesting and difficult to explain with words, but if you are a car person, then you know exactly what I'm talking about.
How many of you, at least at first glance, have been duped into believing that the e39 528i in front of you that sports replica M5 wheels and has an M badge is a poser and not a real M5? How about an E320 masked to look like an E55 AMG? An A6 with an RS badge on the back perhaps? German cars aren't the only ones butchered either. We see normal Integra's badged as Type-R's and standard Lancer's made to look like Evolutions and in the States I even saw a regular Ford Taurus with a SHO badge! But one cannot speak of faux cars and not mention the famous Pontiac Fiero bodykit that made it look like a Ferrari. Almost.
Now, if you're like me, you get irked a bit followed immediately by this thought "you're not fooling me." It is at this point that most people stop and continue about their business. But what about the percentage of people, myself included, that actually take it a step further and have the urge to see the guy/gal that would try to pull off such a masquerade and perhaps, just perhaps, just maybe, yell out "poser!" and then drive/walk away feeling like you did the right thing and that somehow, somewhere, a "1UP" should be recorded next to your name in the sacred records of Automobilus Justificus. Anyone else out there?
The following is a true story and aims to point out yet another type of driver. I have yet to classify such a driver, so if you are feeling creative, please feel free to provide some ideas in the comments below. Now, the story.
The year was 2007 and it was a hot July afternoon. I was in Romania on a road trip with my wife and three other good friends who decided to tag along. As the driver, I had at my disposal a silver, four-door Ford Focus…which was rented. It wasn't fast, but it did the job well and I was happy it was a manual and packed A/C. At one point in our trip, we stopped for fuel in the south-western city of Timisoara. This is where our story gets interesting.
While there, I fueled the Focus and then parked it in order to pay a visit to the mini market and purchase some treats as well as refreshments for the road. Walking towards the shop, I spotted a yellow Lamborghini Gallardo. I was 100% sure it was a Lamborghini Gallardo just as I am 100% sure that water is wet, pancakes are delicious and adding lightness in automobile construction is awesome. Also, since the year was 2007 and the Lambo wasn't a Superleggera , (taking into consideration it might be a 2003-2007 model year) it must have been running the 5.0 V-10 and not the 5.2 V-10 which meant at best it was pushing out 513HP and worst case 493HP.
Walking up to the car I see the door open with the driver inside, texting. I was hoping to engage in some car talk with a fellow auto enthusiast, share some stories and then part ways. See, I am of the belief that all car enthusiasts are made equal and that it doesn't matter whether you drive a raging Reventon or a Reliant Robin. One can respect each other's tastes and opinions in cars, much the same way we can respect tastes and opinions in music that differ from our own. Back to the main subject. Here's how it went down:
Me: Very nice Gallardo!
As Of Yet Unnamed Type Of Auto Enthusiast: Thanks.
Me: The V-10 must sound like a dream.
As Of Yet Unnamed Type Of Auto Enthusiast: It's not a V-10 it's a V-12.
Me: …..
As Of Yet Unnamed Type Of Auto Enthusiast: …..
Now, how many of you are feeling that "irked" sensation that I was talking about earlier? The one where you sense that the force has been disturbed and that somewhere in Sant'Agata Bolognese Italy the spirit of Ferruccio is screaming "No, no, no! Perché? Questo non è possibile!"
I know that there are individuals out there than can afford to purchase supercars knowing little to nothing about them. This driver however, I'm sure, knew the difference between what engine his Gallardo had and the engine that the Murcielago comes with. I've talked with people who genuinely had no idea of the numbers, figures or performance their cars were capable of. They bought them because they were pretty, expensive and hot and when asked about particulars they will honestly reply with "No idea, but it screams like thunder and goes like a jet when I mash the gas pedal." I respect honesty.
However, just so I'm perfectly clear, what I just described above does NOT apply to everyone who owns a supercar. I have many friends and have spoken with many owners of high performance vehicles who are some of the most knowledgeable car people on earth.
So now, we are left with the following question: Why do people blatantly feel the need to be dishonest with their cars? Why do people feel the need to re-badge their vehicles and slap on ///M badges and AMG stickers?
It's now your time to share your poser stories and perhaps your theories on why it is that posers do what they do. And while you're at it, come up with a designation for the As Of Yet Unnamed Type Of Auto Enthusiast that I described above!
Happy Motoring!
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73 responses to "I'm not a poser, I just fake a lot"
Manny Antunes says:
Loved it James!
James Bachici says:
Thanks Manny! It's been bothering me for years :)
Tony Townsend says:
Definitely agree on that. I've got a debadged E36 325is myself and even though in it's current setup, it does look quite a bit like an M3, there's no way any ///M badges are going anywhere on that car. If it wasn't an M3 out of the factory, it isn't an M3.
Thanks for reading and posting Tony. Like you said, if it didn't come out of the factory "born" an M3, then it will never be an M3. Some people believe that since their modded cars are faster than their ///M brothers it deserves the badge. It doesn't.
Vic Tapong says:
I have 328i M sports package and it has M badge on steering wheel and by the door. Does it make me a M poser?
Erik Hansen says:
no, you bought a package (a very good package) that bmw endorses and manufactures. a poser is one who adds performance badges themselves.
No it doesn't. I myself have replaced my steering wheel with an ///M model on my 01 330i as well as CSL wheels. It still says 330i on the back though :)
As Erik Hansen points out, purchasing packages for your car that the company offers is perfectly fine.
InternetPoliceChief says:
As long as the 328 remains on the tail with no ///M next to it, you are pose free. Also, a Batman symbol in lieu of the Roundel does not make you Batman. Not that you claimed to be, but that's another one that bothers me.
Amusingly I did this the other way round. I owned a 1997 M3 Evo 321bhp. I removed the badges and added 316i badges :)
Was fun at Brands Hatch race track surprising a few people! It was a saloon also, so made it look more believable. :) Oh.. and it had nitrous too.. hehe
I don't even know what to say to that! Sounds like you left a lot of people scratching their heads :)
Ramon Juarez says:
I saw recently a 2000 Toyota MR2 with Ferrari STICKERS! Instead of the Toyota badge it had Ferrari stickers. And a friend of mine (who doesn't know ANYTHING 'bout cars) said: LOOK A FERRARI! I turn my head and see that pathetic vehicle parked as a moron. Sad but true.
Howie_in_AZ says:
There is a guy that I occasionally see on my commute with an E46 sedan that has an 'M3' badge on the back. I hope it's actually a 330i ZHP but have the sinking feeling it's really a 325i. I don't understand this at all: the only people who would know the difference between a 325i and an M3 are car people — the same people that know how to spot the sometimes subtle differences between a 325i and an M3. Mere mortal non-car people won't know (or care) about the badge on the back, so why fake the badge anyway? The only people you're "fooling" are the people that know you're not in what you claim to be in.
Anyway, I pulled up next to him one day in my actual M3 and motioned for him to roll down the window. He took off the opposite direction.
Haha! He saw it coming!
I agree with you 100% Howie. Posers are only fooling themselves since non-car people don't know (and don't care) while car people can spot the difference. So really, what's the benefit?
i don't understand why people do that. it's stupid. especially the ones that don't make sense like the "m545i" one shown above. like the car you have. and you can't really go wrong with any bmw even if it is a base engine. and you'll really look stupid when someone asks for a hell ride or a race.
Merlosso says:
Just yesterday I saw an ///M6 badge right underneath the factory "330i" badge on an E46. Tried to get a picture but he turned. To make it even worse (if that's possible), it was on crooked.
Wow, that's like an executive first class poser! To badge out of your Series is something I have yet to see! 325i to M3, seen it. 530i to M5, check…but NEVER have I seen someone but a 6er badge on a 3er! A picture would have been priceless :)
X5SoB says:
This is kind of article that would have generated much commentary in the old blog format, the sheer lack of response shows just how much DISQUS has eroded BMWBlog's fan base. I was even willing to put up with viper in the previous format, this new set up sucks so bad that even he went away. It's truly sad, I rarely feel the urge to post anymore.
Horatiu B. says:
Why? Cause it's more open? Cause we wanna control trolls and spam?
Truong Giang says:
control trolls and spam? then what do you rate Babken and oemcargps?
Their comments have been deleted. Or most of them.
Slimzy says:
haha this made me laugh…reminds me of a story from a while ago when i had my WV golf gti which at the time i had recently debadged …i pulled up to a petrol station where another gti owner was feuling up…as i got out he asked me what i was driving and i replied that it was a wolfsburg edition…he lookd at me blankly and asked what it was….as i explained the conversation moved over to his car and was told eventually that it was a gti replica…disgusted by this i headed to the boot and handed him the bagdes that i had removed from my car
priceless :)))
had to be done in my opinion if you dont own a proper car y destroy a name or brand
A friend drove into a gas station and saw an E class parked in the wash bay with the front hood up. Rear badge shows E320. Went around the front and peek into the engine bay with the owner standing near by, then told the owner " I didn't know merc made 4 pot 320!"
Alexander Karkar says:
I cannot stand people who put an M badge on their Bimmers. It's a disgrace to the M division. A disgrace to the BMW you're driving. Putting an M badge on a regular BMW is like spray painting a black bear white so that you can call it a polar bear.
Oloff says:
Can't agree more. If you can't afford a m don't ruin the name/brand
I even saw a 318i with W/// badge =.=
the.king says:
It's a v18!!!
ahhhh good old Romania, where every e36 316, 318 and 320 has M3 badges. stupid aftermarket bumpers and gastly chrome tail lights. You should not be surprised by the answer, having lived in Timisoara for 5 years I came to understand that even though you have a nice exotic(or any fast/sporty car) doesn't mean you have any idea about it, especially if they are bought second hand. Met a guy with an R8 who told me he had it supercharged, when I asked him to show it to me, he popped the "hood" and told me it's somewhere in there, under the valve cover. When I told him that it's not supercharged, and asked him who told him that, he said that the previous owner had it modified, and that's impossible not to be supercharged,cuz it's so damn fast.
Thanks for reading and sharing Alex! That sounds like another guy I recently met in Romania this past summer. He proceeded to tell me how he purchased a second hand X5 and how the previous owner told him it would do 0-100 km/h in 3 seconds…I had to let him know he was duped.
Catalin357 says:
if I'll buy a M3 , I'll put a badge of a 320i ? how's that called ?:)
giulia86 says:
It's called lying.
not lying, just deception!
or just a fishing technique with a 320 bait….
Rolf says:
haha i see it pretty often here, especially Russians like to do this (put M badge on 524td or something). I hate it, first thing I'd remove on such car. One of my friends though put 525tds badge to a 540i/6.
Why? Smokes like a diesel and doesn't move anywhere (if you know what I mean).
kalmankacso says:
Cousin had a 325tds with M badge on it, M spoiler, some aftermarket bumpers and side skirts. lots of people thought it was an M3 until they heard it was a diesel lol.
Also I've seen 530d E60 that looked like an M5.
Derobbie says:
When I was at the BMW garage, I spotted an 'M5' that had all M-symptoms. Even the small grids on the sides. What tipped me off, however: brake discs.
These are my main point of reference. I asked one of the mechanics and the car turned out to be a 525d and the owner had managed to get hold of original M body parts. (which is still a feat in itself)
But the story turns uglier. 2 weeks after receiving my (badgeless) 550i, I was refueling it (which I have to do quite often with respect to my prior 535d)
A guy in a Maserati Quattroporte pulled up to refuel and started talking about cars. And he proudly showed me his latest purchase: an M5! But with license plates that I had seen before.
Some people are pathetic. If you can't afford one, just accept it. No one will think any less of you.
Wangta says:
I have an E90 M3, but as an M enthusiast, I put an M badge on my Vespa scooter. Am I still a poser? ;)
I'm gonna have to go with "passionately creative" :)
Online you can find a guy in florida that turned a 2002 mercury cougar into a Bugatti Veyron… well the body that is. looks identical but still has the 1.9L one hundred something horsepower of the cougar but looks identical to a Veyron. Very upsetting… CheapER, but upsetting
S85 V10 says:
Generally considered wrong to re-badge car to what it is not.. makes me cringe.. matter of fact i go the other way.. I always de-badge all the M cars i have owned side vents included.. I like understated looks but i know im not fooling anyone with dual exhaust.. when i have seen some wonder IF im a poser.. i usually downshift and take it to high RPMs so there is no mistaking the S85 V10 and i usually get thumbs up..
If the tardy badge placement does not give the poser away the engine will for sure…
Sajan says:
When I come to sell my 316ti compact I will take the 316ti badge off and then hopefully one day (or another lifetime) when I can afford an e60 m5 I will take the m5 badge off and get a '5' badge and then stick the 16i next to it. I'm sure that'll make BMW fans look twice! '516i' imagine seeing that! Everybody knows there's no such thing but imagine seeing a genuine M5 with four angry exhausts coming out the back with a '516i' badge. That'd be amazing
Jp02ti says:
This issue is older than M cars! I have a 1969 BMW 202ti, which was the "hot" 2002 of the day. I've seen so many 2002s badged as "ti" and it makes me really annoyed, since the real factory "ti" version is now very rare, and also easily spied. very annoying!
Indeed! I suppose we can write an entire article just on the hunt for the first poser then :)
Viper says:
subtotal says:
I have an M wheel and shift knob inside, but have no other part of M packet in the car. I've bought the car like this.
I've put a Hamann badge over the 525d factory sticker. I think no enthusiast would think that my Touring really has a Hamann tuning, and I do not care what the others think. It looks good and a nice gag.
Am I a poser?
rone30 says:
yes it does but not because you have the wheel & shifter, because you put a hamann sticker for no reason. a bmw looks good, a sticker wont make it go faster or look better.
The reason I've put it that it looks good above the 525d badge. I did not replace the factory badge just put it right above.
You're right it will not be faster.
I would be a poser if I want to impress others with this. This case putting nice alloys to a car makes someone a poser?
Anyway, do you beleive that a sticker could impress anyone?
Al Bundy says:
Glad I'm not the only one that feels this way. I've never understood it. I am a Porsche/BMW guy and when I see a GT3 RS badge or M badge on a car that clearly isn't that, I can only wonder what went through their brains. I could see adding a badge to a complete POS as a joke, but the people that would be impressed by such a badge would be the ones to know you are full of s—.
7Burgen says:
I saw a 325i with red (insted of blue) and white roundels.Thought of it as more of a conversation piece than an attempt to pose. I know BMW used to produce cars in Eisenacher. Those cars had a red and white roundel. After the war the factory made Wartburgs instead :-(
Is this red and white roundel some historical artifact or just a fashion thing?
Mose121 says:
Why do they do it, well because their posers, duh!!! If you want to put an ///M badge on the trunk of a non-///M car, go right ahead. Just don't get all pissy when I tell you how dumb you appear to me and everyone else that knows what the car really is. Then think about how much dumber you'll look when the people that do know tell the people that don't know, and then they tell you how dumb you are. Boy you are dumb.
Blub says:
poser here -> iambetterthanyou.org
GTA-V-Nut says:
I guy in my neighborhood owns a light blue 2004 BMW 328i, and it's badged as an M3. it's just hilarious. One time I was walking to wal-mart and passed a last generation S class, an S550, and it had AMG to the right of the trunk. It was so stupid, lol.
I seen a Hyundai veloster with a m3 badge on the highway I was laughing to myself pretty good on that one
Wade Foraker says:
You know what else drives me nuts? Someone calling an E60 an E39! Seriously? I mean unless someone essentially reshaped an E39 body using bondo (or styrofoam), that is the rear end of an E60!
The text was not directly related to the photo. Clearly that's an E60.
Piotr says:
I had a e39 535i when bought it had an M badge on the trunk that did not last a second :) after some time I bought a 520i badge :)
Stuart Shone says:
in the eighties i had a holden premier with 4.2 badges on it it had an external front sun visor and was at standard ride height but under the bonnet was a 427 chev not quite standard, the only thing that gave it away a little was the exhaust note , but i had so much fun in it, people would always check out the badges and then try to embarrass me, didn't work to often.
So it was a fake Lamborghini!?
Nope! It was a regular Gallardo being driven by someone who knew nothing about cars, so he'd just tell everyone it was a V-12 because it sounds cooler.
João Resende says:
I really thought this would end eith the Gallardo benigna replica.
But I can really relate eith your story and, I guess, it's a worldwide thing .
It really is. We see it a lot all over
ddunks12 says:
Don't mean to be that guy, but if the beginning of the article is referring to the picture shown that would be an e60 528i, not a e39. E39s look like this:
I'll try posting this image again:
And still didn't work, but nothless, good article, just bugs me that it says e39 when a e60 is clearly being shown
We know. The text wasnt refering to the image.
Okay good lol


text:
import _ from 'lodash'
import AWS from 'aws-sdk'
import childProcess from 'child_process'
import winston from 'winston'
import Credstash from 'credstash'
import deasyncPromise from 'deasync-promise'

const CREDSTASH_PREFIX = 'credstash'

function _fetchCred(name, credstash) {
  return new Promise((resolve, reject) => {
    credstash.get(name, (err, secret) => {
      if (err) {
        reject(err)
      } else {
        resolve(secret)
      }
    })
  })
}

class ServerlessDeployEnvironment {
  constructor(serverless, options) {
    this.serverless = serverless
    this.credstash = new Credstash()

    // Only meaningful for AWS
    this.provider = 'aws'
    this.config = serverless.service.custom.deployEnvironment
    this.options = options
    this.commands = {
      runWithEnvironment: {
        usage: 'Runs the specified command with the serverless environment variables set',
        lifecycleEvents: ['run'],
        options: {
          command: {
            usage: 'The command to run',
            shortcut: 'c'
          },
          stage: {
            usage: 'The stage to use for stage-specific variables',
            shortcut: 's'
          },
          args: {
            usage: 'Extra arguments to pass through to the subprocess',
            shortcut: 'a'
          }
        }
      }
    }
    // Run automatically as part of the deploy
    this.hooks = {
      // Hook before deploying the function
      'before:deploy:createDeploymentArtifacts': () => this._addDeployEnvironment(),
      // Hook before running sls offline
      'before:offline:start': () => this._addDeployEnvironment(),
      // Hook before running sls webpack invoke
      'before:webpack:invoke:invoke': () => this._addDeployEnvironment(),
      // Command hook
      'runWithEnvironment:run': () => this._runWithEnvironment()
    }

    const stage = options.stage || _.get(serverless, 'service.custom.defaults.stage')
    if (!stage) {
      throw new Error('No stage found for serverless-plugin-deploy-environment')
    }
    winston.debug(`Getting deploy variables for stage ${stage}`)

    // TODO: This doesn't belong here, but we need to set the options before populating the new properties.
    serverless.variables.options = options // eslint-disable-line

    // Allow credstash variables to be resolved
    // TODO(msills): Break into a separate plugin
    const delegate = serverless.variables.getValueFromSource.bind(serverless.variables)
    const credstash = this.credstash
    serverless.variables.getValueFromSource = function getValueFromSource(variableString) { // eslint-disable-line no-param-reassign, max-len
      if (variableString.startsWith(`${CREDSTASH_PREFIX}:`)) {
        // If we are not to resolve credstash variables here, just write the variable through unchanged
        if (options.credstash && options.credstash !== 'true') {
          winston.info(`Skipping credstash resolution for variable '${variableString}'`)
          return Promise.resolve(variableString)
        }

        // Configure the AWS region
        const region = serverless.service.provider.region
        if (!region) {
          return Promise.reject(new Error('Cannot hydrate Credstash variables without a region'))
        }
        AWS.config.update({ region })

        const key = variableString.split(`${CREDSTASH_PREFIX}:`)[1]
        return _fetchCred(key, credstash)
      }

      return delegate(variableString)
    }

    if (!serverless.service.custom.deploy) {
      winston.warn('No deploy object found in custom, even though the serverless-deploy-environment plugin is loaded.')
    }

    const deployVariables = _.get(serverless, 'service.custom.deploy.variables', { })
    const deployEnvironment = _.get(serverless, 'service.custom.deploy.environments', { })

    // Explicitly load the variable syntax, so that calls to populateProperty work
    // TODO(msills): Figure out how to avoid this. For now, it seems safe.
    serverless.variables.loadVariableSyntax()
    // Explicitly resolve these here, so that we can apply any transformations that we want
    serverless.service.deployVariables = deasyncPromise(serverless.variables.populateProperty(deployVariables, false))[stage] || { } // eslint-disable-line
    const envs = deasyncPromise(serverless.variables.populateProperty(deployEnvironment, false)) // eslint-disable-line
    serverless.service.deployEnvironment = _.merge(envs.default || { }, envs[stage]) // eslint-disable-line
  }

  async _resolveDeployEnvironment() {
    return this.serverless.service.deployEnvironment
  }

  async _addDeployEnvironment() {
    const env = await this._resolveDeployEnvironment()
    // Make sure that the environment exists (if no environment is specified, it's undefined), and augment it with the
    // scoped environment
    this.serverless.service.provider.environment = _.extend(this.serverless.service.provider.environment, env)
  }

  async _runWithEnvironment() {
    const deployEnv = await this._resolveDeployEnvironment()
    const env = {}
    _.merge(env, process.env, deployEnv) // Merge the current environment, overridden with the deploy environment
    const args = this.options.args || ''
    const output = childProcess.execSync(`${this.options.command} ${args}`, { env, cwd: process.cwd() }).toString()
    for (const line of output.split('\n')) {
      winston.info(`[COMMAND OUTPUT]: ${line}`)
    }
  }
}

module.exports = ServerlessDeployEnvironment

redpajama_set_name: RedPajamaGithub


Hey – Jennifer here. I was diagnosed last week with a 2mm (very small) AN. I happen to work at Cedars-Sinai in Los Angeles so I was able to meet with the Chair of Neurosurgery here yesterday (Keith Black). He highly recommended that we monitor it for the next 6-9 months so I will be getting a baseline hearing test soon and then another in 6 months (my call on not waiting 9) as well as a 2nd MRI at that time. He said the risks of surgery at this point outweigh the benefits. I haven't noticed any hearing loss and only got the MRI because I noticed that I was getting vertigo in specific positions and my husband refused to go to Palm Springs with me unless I found out why (he's kind of a hypochondriac which usually annoys me but paid off for me this time!). Anyway, I'm just not one to worry about things so that's where I am now. I'm just not worried. Should I be? it sounds like hearing loss is almost a definite thing whether I get surgery, radiation, or do nothing (pardon the pun!). Has anyone every had an AN without losing their hearing? Or without experiencing tinnitus or extreme vertigo? It looks like Steve (@sprucey1) is about the only person around here who has come out of this thing just fine. I have found ways to work around the moderate vertigo I have been experiencing by just not putting myself in those specific positions. I am 48, very active, and am otherwise healthy (though I've ALWAYS been super clumsy). I just want to make sure I'm being proactive enough. I would really hate to risk having my active lifestyle altered in any way at all. Any feedback is greatly appreciated!
I have been losing hearing in my right ear very gradually over many years. When I mentioned it to my ENT doctor as I was getting two thyroid nodules examined and needle biopsied (which were small and benign), he had me take a hearing test, then sent me for my first MRI because of the auditory test result pattern. I have a small acoustic neuroma 5 x 11 mm. That was about 3 or 4 years ago. Almost a year later, I had another brain MRI, again without and with shot and was told it had not grown and I could wait 2 years before getting a 3rd MRI and thyroid sonogram, which are due this spring. I do not, so far, have other really noticeable problems, except ringing and clicking in my ear and, maybe, sometimes a few balance problems when turning or bending fast or walking in the dark. If the acoustic neuroma causes worse problems and grows too large, I will have to have radiation. These days, the choices are observation, surgery, or radiation. Since I am now 71, the ENT thinks observation of the small tumor is best, and then radiation, if needed. Also, I have been dealing with annoying irritable bowel and refux for several years, too. Hopefully, by the the time I need the AN treated, better treatments will become available. Right now, I am hoping it does not grow. Good luck with your AN journey! Everyone has different circumstances. Hope my story helps you decide what to do and helps ease your mind somewhat. .
@kkkk – thanks for sharing that. I definitely feel more comfortable knowing that others are simply monitoring rather than treating. I'm not typically a wait and watch kind of person so that was difficult for me to get behind. Sorry about the other issues you are having to deal with. My group has done quite a bit of GI research dealing with IBS and GERD over the years so I know how stressful those things can be for patients. Best of luck to you!
I made a decision I sat out of my life long enough. I was diagnosed in 2014 and stopped working because of my balance issues. I found out what was wrong then. I was going through many health issues at the time. This was the scariest. The one thing the doctors at mayo said I did right was I went every year for a new MRI about the same time. It was able to help them gage how fast the tumor was growing. If it had suddenly changed its speed in growth which it had. Go every 6 months. My neurosurgeon kept saying it wasn't growing but I was showing up with more symptoms. Doctors can be wrong. If I hadn't sent all my records to Mayo Clinic for another open appointment. I wouldn't know today that this was growing and at a faster rate. It was 1.9 centermeters when they took it out. Just a few months before it was 1.4 centimeters. So in 3 months it grew .5 centimeters. It's amazing how they could change and you can suddenly die from these benign tumors. All I know is I wanted something that had the potential to kill me removed.
Jjanes I waited 4 1/2 years before deciding to do something to get rid of my acoustic neuroma. Don't wait. That's my advice to anyone first diagnosed. These cause so much damage to our bodies than hearing loss. Depression anxiety from isolation when you're in a noisy situation and you can't hear the people that are next to your good ear with a hearing aid. The loss of hearing sometimes is both ears. Insurance companies don't help. They make it difficult to get a cochlear nerve implant. You have to have less than 50% hearing all the time to get an implant. If it wasn't for my doctor doing a Peer to Peer I wouldn't have gotten one and I was at 45% most of the time. I was denied at first then he appealed with a peer to peer. They approved it. Thank you Dr Carlson!!! These Doctors care about their patients! I know first hand that there is unbelievable things happening at Mayo Clinic. Don't be complacent to stay in your area. Collect everything you all ready have call Mayo get a patient number write them a number and tell them you want doctor Matthew Carlson to take a look at everything. Tell him you want to preserve your hearing. Get rid of the tumor. If he isn't the doctor to do this he will send you to the guy that is the right one.
Hi Tracy: My name is Rose. I was diagnosed with a acoustic neuroma in 2014. Very scary indeed. I went in to my doctor with an earache. They gave me 2 weeks of antibiotics . After that 2 weeks the side of my head still hurt and I was loosing balance. I went back and was sent to a ENT who did an ear biopsy, nasal passage biopsy and an MRI in a 3 week period.
The biopsy showed nothing as well as the nasal scan. But the MRI came back showing a 3.4 centimeter tumor in my ear canal and growing and pushing my brain stem in too the point that they saw Hydrocephalus(water on my brain). I had no headaches after the earache no pain or dizziness , nor imbalance. There were no symptoms at all.
Three weeks later I met with my surgeons and neurosurgeons and they explained the surgery to me and my children. It was a scary time, BUT I have a daughter who is an RN and any questions I didn't know to answer she did, and by the time my surgery was scheduled, we had all our answers and were as prepared as we could be.
I was diagnosed February 9, 2014, and my surgery was scheduled for March 10, 2014. I am allergic to a lot of the Anastasia drugs they give people when in surgery, so I had to make many scheduled appointments to the neurosurgeons for testing. My surgery was 12 hours. In this 12 hour period I had 4 surgeons and neurosurgeons flip flopping thru my surgery. When one completed their part they came out and updated my daughter so she could update my family as too progress. It took the full 12 hours.
the amazing part is with this surgery it was non cancerous and they scraped on my nerves for 12 hours and needed to leave 2% of the tumor on my brain stem or it would have severed nerves for my face and eye. I am 2 years since this surgery and I still need to see neurosurgeons for MRI and testing. I have imbalance due to the hearing loss in my left ear where the surgery was performed. Things that never bothered me before do now, my balance is still off and will always be off. I just need to adjust my employment and life to the after effects of having needed brain surgery.
Every day is a challenge, but I by the Grace of the Good Lord am alive.
Hi Rose you are a very blessed person. So they did surgery and then the radiation on you. Didn't you ever get physical therapy for your vestibular system? Thats what I'm going through now to bring it back to normal. It can be retaught. Ask for orders from your family doctor or ENT. This could change your life!
I recently was diagnosed with an Acoustic Neuroma. I am seeing Dr Thompson in Omaha NE. I am scared to death how this is going to turn out. I have lost a lot of my hearing in my right ear now.
Today I am also dizzy. I have an appointment with the Dr on the 17th for another MRI and a simulation. He is planning to do a one time radiation treatment the following week. What have you found out about this tumor?
When I went to Mayo Clinic I was offered the Radiation Surgery. It is supposed to stop the acoustic neuroma from growing any bigger. It works well for some people but not at at all for others. Because of this I decided against it. I wanted this thing completely out of my body. I chose durgery. My acoustic neuroma tumor was still small enough to remove easily. I didn't want further issues than I already have been experiencing. Hypothyroidism neuropathy dead in the left ear 45% hearing in the right ear. I didn't want paralysis of my face or worse death. So radiation was a no for me. Ad long as they could taint all out I wanted surgery. This is something you need to choose for you. If your doctor doesn't perform surgery well and you prefer that then go get another opinion. I did and the out come has been fantastic!!
I went to Mayo for what I thought was vertigo, they did a hearing test and I was told by the doctor I had a tumor and needed a MRI. I can't have MRI's due to a metal implant in my body so they did a cat scan. I was told it was normal, however I just recently started having some light vertigo. I'm a flight attendant so even slight vertigo can have an impact but also flying can cause vertigo. So my question was was the cat scan as effective as a MRI and could they have missed ti?
I did phone Mayo when I first found that I have an AN, but my insurance at that time was not good in that area. Mayo's office said I could have a DVD of the MRI sent to them.for a second opinion. After my next MRI, I plan to get all three MRIs on a DVD and send them to Mayo's. Now I am on Medicare and a supplement, which should cover better.
Hearing loss seems to be what happens first and balance issues. Depending on the size of the tumor, worse symptoms can occur. My ENT doctor said that radiation is recommened for people past 65 and surgery for younger patients. Smaller tumors are watched with MRIs. 2 percent of radiation cases get cancer, which does not make me very happy, especially since I am already 1 in 100,000 with a AN! When there is surgery or radiation, even worse problems can occur.
That is a question for the doctors.
I had brain surgery in 2014 for a 3.5 centimeter tumor on my brain stem. Had the 12 hour surgery, still have 2 percent on my brain stem . Since my surgery , I've bad balance issues due to hearing loss in left side. Depending on barometer depends on my day, good, bad or other issues. I wish you all the luck in the world and many many blessings.
Hi vickid117, I was diagnosed with an acoustic neuroma a year ago, and I am currently using the observation approach. My neuroma took my hearing in my right ear many years ago. About 3 years ago I got Phonak hearing aids like what you describe, one side hearing aid and the other side a transmitter. At first, I had trouble locating where sound was coming from, but that has gotten much better with time as my brain has adjusted. Hopefully yours will, too. Also, when I go somewhere loud like a sporting event or concert, I usually just turn the hearing aids off. I saw Steven Tyler in January, and it was great! Keep living life to the fullest! Good luck!


Second World War fighter pilot Eric Carter is one of only four surviving members of a secret mission, code-named 'Force Benedict'. Sanctioned by Winston Churchill in 1941, Force Benedict was dispatched to defend Murmansk, the USSR's only port not under Nazi occupation. If Murmansk fell, Soviet resistance against the Nazis would be hard to sustain and Hitler would be able to turn all his forces on Britain...Force Benedict was under the command of New Zealand-born RAF Wing Commander Henry Neville Gynes Ramsbottom-Isherwood, who led two squadrons of Hurricane fighters, pilots and ground crew which were shipped to Russia in total secrecy on the first ever Arctic Convoy. They were told to defend Murmansk against the Germans 'at all costs'. 'We all reckoned the government thought we'd never survive' - but Eric Carter did, and was threatened with Court Martial if he talked about where he'd been or what he'd done. Now he reveals his experiences of seventy years ago in the hell on earth that was Murmansk, the largest city north of the Arctic Circle. It will also include previously unseen photos and documents, as well as exploring - for the first time - other intriguing aspects of Force Benedict.


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text:
\section{Introduction}

The problem of synthesizing content by example is a classic problem in computer vision and graphics. It is of fundamental importance to many applications, including creative tools such as high-level interactive photo editing \cite{Agarwala04,Barnes09,Hertzmann01}, as well as scientific applications, such as generating stimuli in psycho-physical experiments~\cite{Gatys2015b}.

\begin{figure}
	\centering
		\includegraphics[width=1\linewidth]{teaser2.png}
		\caption{By combining deep convolutional neural network with MRF prior, our method can transfer both photorealistic and non-photorealistic styles to new images. Images credited to flickr users \emph{mricon} (A) and \emph{Peter Dahlgren} (B).}
	\label{fig:teaser}
\end{figure}

In this paper, we specifically consider the problem of data-driven image synthesis: Given an example image, we want to fully automatically create a variant of the example image that looks similar but differs in structure. The intended deviation is controlled by additional constraints provided by the user, ranging from just changing image dimensions to detailed layout specifications. Concretely, we implement this by splitting up the input into a \emph{``style''} image and a \emph{``content''} image~\cite{Gatys2015b,Hertzmann01}. The first describes the building blocks the image should be made of, the second constrains their layout. Figure~\ref{fig:teaser} shows an example of style transferred images, where the input images are shown on the left. Our results are are shown on the right. Notice our method produces plausible results for both art to photo and photo to art transfers. In particular, see how meso-structures in the style images, such as the mouth and eyes, are intentionally reused in the synthesized images.

The classic data-driven approach to generative image modeling is based on Markov random fields (MRFs): We assume that the most relevant statistical dependencies in an image are present at a local level and learn a distribution over the likelihood of local image patches by considering all local $k \times k$ pixel patches in the example image(s). Usually, this is done using a simple nearest-neighbor scheme \cite{Efros99}, and inference is performed by approximate MRF inference \cite{Efros01,Kwatra05,Kwatra03} or greedy approximations \cite{Barnes09,Efros99,Hertzmann01,Wei00}.

\begin{figure*}[t]
	\centering

		\includegraphics[width=1\linewidth]{FigurePCA.png}
		\caption{The input image is encoded by the VGG network (pixel colors show a 3D PCA embedding of the high-dimensiontal feature space). Related image content is mapped to semi-distributed, approximately spatially coherent feature constellations of increasing invariance \protect\cite{Zeiler14}. Input image credited to flickr user \emph{Emery Way}.}
	\label{fig:PCAface}
\end{figure*}

A critical limitation of MRFs texture synthesis is the difficulty of learning the distribution of plausible image patches from example data. Even the space of local $k \times k$ image patches (typically: $k \approx 5...31$) is already way too high-dimensional to be covered with simple sampling and nearest-neighbor estimation. The results are mismatched local pieces, which are subsequently stitched~\cite{Kwatra03} or blended~\cite{Kwatra05} together in order to minimize the perceptual impact of the lack of ability to generalize. The missing ingredient is a strong scheme for interpolating and adapting images from very sparse example sets of sample patches.

In terms of invariance and ability to generalize, discriminatively trained deep convolutional neural networks have recently made dramatic impact~\cite{Krizhevsky2012,Simonyan14c}. They are able to recognize complex classes of image features, modeling non-linear deformations and appearance variations far beyond the abilities of simple nearest neighbors search.

However, the discriminative design poses a problem: The corresponding dCNNs compress image information progressively over multiple pooling layers to a very coarse representation, which encodes semantically relevant feature descriptors in a semi-distributed (not spatially localized) fashion (Figure~\ref{fig:PCAface}). While an inverse process can be defined \cite{Denton15,Dong15,Gatys15,Gatys2015b,Gauthier15,Mahendran15,Xu15,Zeiler14}, it reamins difficult to control: For example, simply maximizing the class-excitation of the network leads to hallucinatory patterns~\cite{Mordvintsev15}. Rather than that, we need to reproduce the correct statistics for neural encoding in the synthesis images.

Addressing this problem, Gatys et al.~\cite{Gatys15,Gatys2015b} have recently demonstrated remarkable results for transferring styles to guiding ``content'' images: Their method uses the filter pyramid of the VGG network~\cite{Simonyan14c} as a higher-level representation of images, benefitting from the vast knowledge acquired through training the dCNN on millions of photographs. Then, feature layout is simply controlled by penalizing the difference of the high-level neural encoding of the newly synthesized image to that of the content image. Further, the process is regularized by matching feature statistics of the ``style'' image and the newly synthesized picture by matching the correlations across the various filter channels, captured by a Gram matrix. The method yields very impressive results for applying artistic styles of paintings to photographs~\cite{Gatys15}. However, strict local plausibility remains difficult. In particular, using photographs as styles does not yield plausible results because only \emph{per-pixel} feature correlations are captured at different layers and the spatial layout is constrained too weakly.

Our paper augments their framework by replacing the bag-of-feature-like statistics of Gram-matrix-matching by an MRF regularizer that maintains local patterns of the ``style'' exemplar: MRFs and dCNNs are a canonical combination --- both models crucially rely on the assumption of locally correlated information and translational invariance. This equips the encoding of features in a dCNN with approximate Markovian consistency properties: Local patches have characteristic arrangements of feature activations to describe objects, and higher-up encoding becomes more invariant under in-class variation (Figure~\ref{fig:PCAface}).
We therefore use the generative MRF model on such higher levels of the network (\textit{relu3\_1} and \textit{relu4\_1} of the 19-layer VGG network\footnote{Notice that in Figure~\ref{fig:PCAface} layer \textit{relu5\_1} shows the most discriminative encoding for single pixels. However, in practice we found using $3 \times 3$ patches at layer \textit{relu4\_1} produce the best synthesis results. Intuitively, using patches from a slightly lower layer has similar matching performance, but permits overlapped MRFs and increased details in synthesis.}). This prescribes a plausible local layout of objects in terms of more abstract categories and, importantly, tries to ensure a consistent encoding of these higher-level features. The actual task of generalizing within object categories and plausible blending is then performed by the dCNN's lower levels via inversion~\cite{Mahendran15}.

Technically, we implement the additional MRF prior by an additional energy term that models Markovian consistency of the upper layers of the dCNN feature pyramid. We then utilize the EM algorithm of Kwatra et al.~\cite{Kwatra05} for MRF optimization: It easily integrates in the variational framework. Further, we will show that higher-level neural encodings are more perceptually linear, which matches well with the linear blending approach of the M-step.

We apply our method to a number of photo-realistic and non-photo-realistic image synthesis tasks, and show it is able to generalize among image patches far beyond the abilities of classic MRFs. In style-transfer scenarios, the combined method additionally benefits from the abilities of the dCNN to match semantically related image portions automatically, without user annotations. In comparison to previous methods that invert dCNNs, the MRF prior improves local plausibility of the feature layouts, avoiding hallucinatory artifacts and usually providing a more plausible meso-structure than the statistical approach of Gatys et al.~\cite{Gatys15}. In particular, we can strongly improve the plausibility of synthesizing photographs, which was not possible with the previous methods.

\section{Related Work}

\textbf{Image synthesis with neural networks:}
The success of dCNNs in discriminative tasks~\cite{ILSVRC15} has also raised interest in generative variants. Zeiler et al.~\cite{Zeiler14} introduce a deconvolutional network to back-project neuron activations to pixels. Similarly, Mahendran and  Vedaldi~\cite{Mahendran15} reconstruct images from the neural encoding in intermediate layers. The work of Gatys et al~\cite{Gatys15}, detailed above, can also be employed in unguided settings~\cite{Gatys2015b}, outperforming traditional parametric texture synthesis which only uses a linear feature bank and no statistical priors~\cite{Portilla00}.

A further approach is the framework of generative adversarial networks~\cite{Goodfellow2014}. Here, two networks, one as the discriminator and other as the generator iteratively improve each other by playing a minnimax game. In the end the generator is able to produces more natural images than naive image synthesis. However, in many cases the output quality is still rather limited. Gauthier et al.~\cite{Gauthier15} extend this model by a Laplacian pyramid. This leads to clear improvement on output quality. Nonetheless, training for large images remains expensive and the results often still lack structure. Denton et al.~\cite{Denton15} extend the model to a conditional setting, limited to generating faces. It is also possible to re-train networks for specific generative tasks, such as image deblur~\cite{Xu15}, super-resolution ~\cite{Dong15}, and class visualization~\cite{Lu15}.

\textbf{MRF-based image synthesis:}
MRFs are the classic framework for for non-parametric image synthesis~\cite{Efros99}.
As explained in the introduction, a key issue is adapting local patches beyond simple stitching~\cite{Kwatra03} or blending~\cite{Kwatra05}, and our paper focuses on this issue. Aside from this, MRF models suffer from a second, significant limitation: Local image statistics is usually not sufficient for capturing complex image layouts at a global scale. While local details appear plausible, the global arrangement often merely resembles an unstructured ``texture soup''. Multi-resolution synthesis~\cite{Hertzmann01,Kwatra05,Wei00} provides some improvement here (and we adapt this in our method, too), but a principled solution requires additional high-level constraints. These can be either explicitly provided by the user~\cite{Agarwala04,Barnes09,Efros01,Gatys15,Hertzmann01,Lee10,Xie07}, or learned from non-local image statistics~\cite{He12,Li15,Zhang13}. Long range correlations have also been modeled by spatial LTSM neural networks; results so far are still limited to semi-regular textures~\cite{Theis2015}.
 Our paper opts for the first, simpler solution of explicit layout constraints through a ``content'' image~\cite{Gatys15,Hertzmann01} --- in principle, learning of global layout constraints is mostly orthogonal to our approach.

\section{Model}
We now discuss our combined MRFs and dCNNs model for image synthesis. We assume that we are given a style image, denoted by $\bv{x}_s \in \mathbb{R}^{w_s \times h_s}$, and a content image $\bv{x}_c \in \mathbb{R}^{w_c \times h_c}$ for guidance. The (yet unknown) synthesized image is denoted by $\bv{x} \in \mathbb{R}^{w_c \times h_c}$. We transfer the style of $\bv{x}_s$ into the layout of $\bv{x}_c$ by making the high-level neural encoding of $\bv{x}$ similar to $\bv{x}_c$, but using local patches similar to those of $\bv{x}_s$. The latter is the MRF prior that maintains the encoding of the style.
Formally, $\bv{x}$ minimizes the following energy function:
\begin{eqnarray}
\bv{x}&= &\argmin{x}E_{s}(\Phi(\bv{x}), \Phi(\bv{x}_{s})) + \nonumber \\&& \alpha_{1}E_{c}(\Phi(\bv{x}), \Phi(\bv{x}_{c})) + \alpha_{2}\Upsilon(\bv{x})
\label{eq:energy}
\end{eqnarray}
$E_{s}$ denotes the style loss function (MRFs constraint), where $\Phi(\bv{x})$ is $\bv{x}$'s feature map from some layer in the network.
$E_{c}$ is the content loss function. It computes the squared distance between the feature map of the synthesis image and that of the content guidance image $\bv{x}_{c}$. As shown in~\cite{Gatys15,Mahendran15}, minimizing $E_{c}$ generates an image that is contextually related to $\bv{x}_{c}$. The additional regularizer $\Upsilon(\bv{x})$ is a smoothness prior on the reconstruction. Next, we explain how to define these terms in details.

\textbf{MRFs loss function}: Let $\Psi(\Phi(\bv{x}))$ denote the list of all local patches extracted from $\Phi(\bv{x})$ -- a specified set of feature maps of $\bv{x}$. Each ``neural patch'' is indexed as $\Psi_i( \Phi(\bv{x}) )$ and of the size $k \times k \times C$, where $k$ is the width and height of the patch, and $C$ is the number of channels for the layer where the patch is extracted from.
We set the energy function to
\begin{equation}
E_{s}(\Phi(\bv{x}), \Phi(\bv{x}_{s})) = \sum_{i = 1}^{m}||\Psi_i(\Phi(\bv{x})) - \Psi_{NN(i)}(\Phi(\bv{x}_{s}))||^{2}
\label{eq:mrf}
\end{equation}
Here $m$ is the cardinality of $\Psi(\Phi(\bv{x}))$.
For each patch $\Psi_i(\Phi(\bv{x}))$ we find its best matching patch $\Psi_{NN(i)}(\Phi(\bv{x}_{s}))$ using normalized cross-correlation over all $m_s$ example patches in $\Psi(\Phi(\bv{x}_{s}))$:
\begin{equation}
NN(i) := \argmin{j=1,...,m_s}\frac{\Psi_i(\Phi(\bv{x}))\cdot\Psi_j(\Phi(\bv{x}_{s}))}{|\Psi_i(\Phi(\bv{x}))|\cdot|\Psi_j(\Phi(\bv{x}_{s}))|}
\label{eq:ncorr}
\end{equation}
We use normalized cross-correlation to achieves stronger invariance. The matching process can be efficiently executed by an additional convolutional layer (explained in the implement details). Notice although we use normalized cross-correlation to find the best match, their Euclidean distance is minimized in Equation~\ref{eq:mrf} for producing an image that is visually close to the reference style.

\textbf{Content loss function}: $E_{c}$ guides the content of the synthesized image by minimizing the squared Euclidean distance between $\Phi(\bv{x})$ and $\Phi(\bv{x}_{c})$:
\begin{equation}
E_{c}(\Phi(\bv{x}), \Phi(\bv{x}_{c})) = ||\Phi(\bv{x}) - \Phi(\bv{x}_{c})||^{2}
\label{eq:content}
\end{equation}

\textbf{Regularizer}: There is significant amount of low-level image information discarded during the discriminative training of the network. In consequence, reconstructing an image from its neural encoding can be noisy and unnatural. For this reason, we penalize the squared gradient norm~\cite{Mahendran15} to encourage smoothness in the synthesized image:
\begin{equation}
\Upsilon(\bv{x}) = \sum_{i, j}\left((x_{i, j + 1} - x_{i, j})^{2} + (x_{i + 1, j} - x_{i, j})^{2}\right)
\label{eq:tv}
\end{equation}
\textbf{Minimization}: We minimize Equation~\ref{eq:energy} using back-propagation with Limited-memory BFGS. In particular, the gradient of $E_{s}$ with respect to the feature maps is the element-wise difference between $\Phi(\bv{x})$ and their MRFs based reconstruction using patches from $\Phi(\bv{x}_{s})$. Such a reconstruction is essentially a texture optimization process~\cite{Kwatra05} that uses neural patches instead of pixel patches. It is crucially important to optimize this MRF energy at the neural level, as the traditional pixel based texture optimization will not be able produce results of comparable quality.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.75\linewidth]{analysis_matching2.png}
	\caption{Comparison of patch matching at different layers of a VGG network.}\label{fig:analysis_matching}
\end{figure}

\textbf{Weight} The $\alpha_{1}$ and $\alpha_{2}$ are weights for the content constraint and the natural image prior, respectively. We set $\alpha_{1} = 0$ for non-guided synthesis. By default we set $\alpha_{1} = 1$ for style transfer, while user can fine tune this value to interpolate between the content and the style. $\alpha_{2}$ is fixed to $0.001$ for all cases.

\section{Analysis}
Our key insight is that combining MRF priors with dCNN can significantly improve synthesis quality. This section provides a detailed analysis of our method from three perspectives: we first show compared to pixel values, neural activation leads to better patch matching and blending. We then show how MRFs can further improve the results.

\subsection{Neural Matching}
A key component of non-parametric image synthesis is to match the synthesized data with the example. (Figure~\ref{fig:analysis_matching} is a toy example that shows neural activation gives better matching than pixels. The task is to match two different car images. The first column contains the query patches from one car; every other column shows the best matching in the other car, found at different feature maps (including the pixel layer).

It is clear that patch matching using pixels or neural activation at the lower layers (such as \textit{relu2\_1}) is sensitive to appearance variation. The neural activations at layers \textit{relu3\_1} and \textit{relu4\_1} give better results. The top layers (\textit{relu5\_1}) seem to decrease the match quality due to the increasing invariance against appearance variation. This is not surprising because the features at middle layers are usually trained for recognizing object parts, as discussed in~\cite{Zeiler14}. For these reason, we use neural patches at layers \textit{relu3\_1} and \textit{relu4\_1} as MRFs to constrain the synthesis process.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{analysis_blending.png}
	\caption{Linear blending behave differently in pixel space and in neural space. Input photos credited to: \emph{rockysretreat.com} (dog A), \emph{wall321.com} (dog B), flickr user \emph{Jsome1} (cat A), flickr user \emph{MendocinoAnimalCare} (cat B).}\label{fig:analysis_blending}
\end{figure}

\subsection{Neural Blending}
The least-squared optimization for minimizing the texture term ($E_{s}$ in Equation~\ref{eq:mrf}) leads to a linear blending operation for overlapping patches. Here we show that blending neural patches often works better than directly blending pixel patches. Specifically, we compare two groups of blending results: The first method is to directly blend the pixels of two input patches. The second method passes these patches through the network and blend their neural activations at different layers. For each layer, we then reconstruct the blending result back into the pixel space using the method described in~\cite{Mahendran15}.

Figure~\ref{fig:analysis_blending} compares the results of these two methods. The first two columns are the input patches A and B for blending. They are intentionally chosen to be semantically related and structurally similar, but are significantly different in pixel values. The third column shows the average of these two patches. Each of the remaining column shows the reconstruction of the blending at a different layer. It is clear that pixel based and neural based blending give very different results: averaging the pixels often gives strong ghost artifacts. This can be reduced as we dive deeper into the network: through experiments we observed that the middle level layers such as \textit{relu3\_1} and \textit{relu4\_1} often give more meaningful blendings. Lower layers such as \textit{relu2\_1} behave similarly to pixels; reconstruction from layers beyond \textit{relu4\_1} tends to be too fuzzy to be used for synthesis. This also matches our previous observation of middle layers' privilege for patch matching -- because a representation that gives better discriminative performance is more robust to noise and enables better interpolation.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{analysis_mrf.png}
	\caption{Effect of MRFs prior in neural based synthesis. We do not show the example patches here because they are almost visually identical to the synthesized patches cropped from our results (top). In contrast, patches cropped from~\cite{Gatys15}'s synthesis results have severe distortion and smear (bottom).}\label{fig:analysis_mrf}
\end{figure}

\subsection{Effect of the MRF Prior}
Despite the advantages in patch matching and blending, it is still possible for a dCNN to generate implausible results. For example, the matched patches from different layers might not always fire at the same place in the image space, indicated by the offsets between the patches found at layer
\textit{relu3\_1} and \textit{relu4\_1} (Figure~\ref{fig:analysis_matching}). The blending may also produce artifacts, for example, the destructed face of the dog (\textit{relu4\_1}, figure~\ref{fig:analysis_blending}) and the ghosting eyes of the cat (\textit{relu3\_1}, figure~\ref{fig:analysis_blending}). Extreme cases can be found at~\cite{Mordvintsev15} which produces hallucinogenic images by simply stimulating neural activations without any constraint of the natural image statistics.

More natural results can be obtained through additional regularizer such as smothness (total variation) or the Gram matrix of pixel-wise filter responses~\cite{Mahendran15,Gatys15}.
Our approach adds an MRF regularizer to the middle / upper levels of the network.
To show the benefits of the MRFs prior, we compare the synthesis results with and without the constraint. We compare against the ``style constraint'' based on matching Gram matrices from~\cite{Gatys15}. Figure~\ref{fig:analysis_mrf} validates the intended improvements in local consistency. The first row shows image patches cropped from our results. They are visually consistent to the patches in the original style images. In contrast,~\cite{Gatys15} produces artifacts such as distortions and smears. The new MRF prior reduces flexibility in local adaptation in favor of reproducing meso-scale features more faithfully.

\section{Implementation Details}
This section describes implementation details of our algorithm~\footnote{We release code at: https://github.com/chuanli11/CNNMRF}. We use the pre-trained 19-layer VGG-Network from~\cite{Simonyan14c}. The synthesis $\bv{x}$ is initialized as random noise, and iteratively updated by minimizing Equation~\ref{eq:energy} using back-propagation. We use layer \textit{relu3\_1} and \textit{relu4\_1} for MRFs prior, and layer \textit{relu4\_2} for content constraint.

For both layer \textit{relu3\_1} and \textit{relu4\_1} we use $3 \times 3$ patches. To achieve the best synthesis quality we set the stride to one so patches are very densely sampled. The patch matching (Equation~\ref{eq:ncorr}) is implemented as an additional convolutional layer for fast computation. In this case patches sampled from the style image are treated as the filters. The best matching of a query patch is the filter that gives the maximum response. We can pre-computed the magnitude of the filters ($||\Psi_i(\Phi(\bv{x}_{t}))||$ in Equation~\ref{eq:ncorr}) as they will not change during the synthesis. Unfortunately the magnitude of patches in the synthesized image ($||\Psi_i(\Phi(\bv{x}))||$) needs to be computed on the fly.

In practice we use a multi-resolution process: We built a image pyramid using the scaling factor of two, and stop when the longest dimension of the synthesized image is less than 64 pixels. The reference texture and reference content images are scaled accordingly. We perform 200 iterations for each resolution, and the output of the previous resolution is bi-linearly up-sampled as the initialization for the next resolution. We implement our algorithm under the Torch framework. Our algorithm take about three minutes to synthesis an image of size $384 \times 384$ with a Titan X GPU.

To partially overcome the perspective and scale difference between the style and the content images, we sample patches from a number of copies of the style image with different rotations and scales: we use seven scales $\{0.85, 0.9, 0.95, 1, 1.05, 1.1, 1.15\}$, and for each scale we create five rotational copies $\{-\frac{\pi}{12}, -\frac{\pi}{24}, 0, \frac{\pi}{24}, \frac{\pi}{12}\}$. Since increasing the number of patches is computational expensive, in practice we only use the rotational copies for objects that can deform -- for example faces.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{qlt_compare1.png}
	\caption{Comparison with Gatys et al.~\cite{Gatys15} for artistic synthesis. Content images credited to flickr users \emph{Christopher Michel} (top) and \emph{Peter Dahlgren} (bottom).}\label{fig:qlt_compare1}
\end{figure}

\section{Results}
This section discusses our results. Here we focus on style transfer and refer readers to our supplementary material for the results of un-guided synthesis. As discussed later in this section, our method has strong restriction with the input data, so a formal user study is less meaningful. For this reason, our evaluation is performed by demonstrating our method with a number of typical successful and failure cases, with comparisons and discussions related to the state of the art neural based image stylization method~\cite{Gatys15}. We refer readers to our supplementary report for more results.

Figure~\ref{fig:qlt_compare1} shows two examples of stylizing photos by artwork. The input photos (left) are stylized using Pablo Picasso's ``Self-portrait 1907'' and Wassily Kandinsky's ``Composition VIII'', respectively. In both cases, Gatys et al.'s~\cite{Gatys15} method (middle) achieves interesting results, preserving the content of the photos and the overall look and feel of the artworks. However, there are weaknesses on closer inspection: The first result (top row) contains many unnecessary details, and the eyes look unnatural. Their second result lost the characteristic shapes in the original painting and partially blends with the content exemplar. In contrast, our first result synthesized more plausible facial features. In the second result, our method also resembles the style better: notice the important facial features such as eyes and the mouth are synthesized as simple shapes, and the hair as regions of dark color.

Figure~\ref{fig:qlt_compare2} shows two examples of photorealsitic synthesis. We transfer the style of a vintage car to two different modern vehicles. Notice the lack of photo-realistic details and strong smears in~\cite{Gatys15}'s results. With the MRFs constraint (right) our results are closer to photorealistic.

From an informal user study, we observed that \cite{Gatys15} usually keeps the content better, and our method produces more accurate styles. Figure~\ref{fig:qlt_compare3} gives detailed analysis between these two different characters. Here we show three patches from the content image (red boxes), and their closet matches from the style image and the synthesis images (using neural level matching). Notice our method produces more plausible results when a good match can be found between the content and the style images (the first patch), and performs less well when mis-matching happens (the second patch, where our synthesis deviates from the content image). For the third patch, there is no matching can be found for the car. In this case, our method replaces the car with texture synthesis, while \cite{Gatys15} keeps the car and renders it with artifacts. In general, our method creates more stylish images, but may contain artifacts when the MRFs do not fit the content. In contrast, the parametric method \cite{Gatys15} is more adaptable to the content, but at the cost of deviating from the style.

\begin{figure*}[ht]
	\centering
	\includegraphics[width=0.95\linewidth]{qlt_compare6.png}
	\caption{Comparison with Gatys et al.~\cite{Gatys15} for photo-realistic synthesis. Input images credited to flickr users \emph{Brett Levin}, \emph{Axion23} and \emph{Tim Dobbelaere}.}\label{fig:qlt_compare2}
\end{figure*}

\begin{figure*}[ht]
	\centering
	\includegraphics[width=0.95\linewidth]{qlt_compare5.jpg}
	\caption{Detailed analysis of the difference between our results and Gatys et al.'s~\cite{Gatys15}'s results. Input images credited to flickr users \emph{Eden, Janine and Jim} and \emph{Garry Knight}.}\label{fig:qlt_compare3}
\end{figure*}

\subsection{Limitations}
Our method is an interesting extension for image based style transfer, especially for photorealistic styles. Nonetheless, it has many limitations. First and for most, it is more restricted to the input data: it only works if the content image can be re-assembled by the MRFs in the style image. For example, images of strong perspective or structure difference are not suitable for our method.

Figure~\ref{fig:limit} shows two further example cases where~\cite{Gatys15} works better: The first example aim to transfer the style of a white dog according to the content of a yellow dog. Notice how the dogs' facial expression differ in these images. In this case our result (right) failed to reproduce the open mouth in the content image. The second example shows a artistic style that can be more easily transferred by the parametric method: Notice how the textures adapt to the content more naturally in~\cite{Gatys15}'s method. In contrast, our method tries to ``reshuffle'' building blocks in the style image and lost important features in the content image. In general, our method works better for subjects that allows structural deformation, such as faces and cars. For subjects that have strict symmetry properties such as architectures, it is often that our method will generate structural artifacts. In this case, structural statistics such as~\cite{He12} may be used for regularizing the synthesized image.

Although our method achieved improvement for photorealistic synthesis, it is still not as sharp as the original photos. This is due to the loss of non-discriminative image details during the training of the network. This opens an interesting future work that how the dCNN can be retrained, or incorporated with stitching based texture synthesis such as~\cite{Kwatra03} for achieving pixel-level photorealism.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{limit.png}
	\caption{Cases where Gatys et al ~\cite{Gatys15} works better.}\label{fig:limit}
\end{figure}

\section{Conclusions}
The key insight of this paper is that we can combine the discriminative power of a deep neural network with classical MRFs based texture synthesis. We developed a simple method that is able to produce encouraging new results for style transfer between images. We analyzed our results with a number of typical successful and failure cases, and discussed its pros and cons compared to the state of the art neural based method for transferring image styles. Importantly, our results often preserve better mesostructures in the synthesized image. For this first time, this permits transferring photo-realistic styles with some plausibility. The stricter control of the mesostructure is also the biggest limitation at this point: The MRF prior only offers advantages when style and content images consists of similarly shaped elements without strong changes in perspective, size, or shape, as covered by the invariance of the high-level neural encoding. Otherwise, artifacts might occur. For pure artistic styles, the increased rigidity can then be a disadvantage.

Our work is only one step in the direction of leveraging deep convolutional neural networks for improving image synthesis. It opens many interesting questions and future work such as how to resolve the incompatibility between the structure guidance and the MRFs~\cite{He12}; how to more efficiently study and transfer the middle level style across a big dataset; and how to generate pixel-level photorealistic images by incorporating with stitching based texture synthesis~\cite{Kwatra03}, or with generative training of the network.

\section*{Acknowledgments}
This work has been partially supported by the Intel Visual Computing Institute and by the International Max Planck Research School for Computer Science. We thank Hao Su and Daniel Franzen for inspiring discussions.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
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Przypisy

Bibliografia
 Riccardo Niccoli, Samoloty, Bellona 2001.

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Francuskie samoloty transportowe

redpajama_set_name: RedPajamaWikipedia


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