text:
Tobias Rustat
Tobias Rustat (1608-1694) was a courtier to Charles II and one of Jesus College's most significant benefactors.
Tobias Rustat was the son of Robert Rustat (d.1637), who had been a student at Jesus in the 1580s and who had succeeded his own father as vicar of Barrow-on-Soar in Leicestershire.
Rustat was first apprenticed to a barber surgeon and then joined the household of Viscount Fielding, a Leicestershire landowner. There he showed himself "the most diligent attending servant in the whole family, early and late, exact and complete in his place", spending the years 1634-39 in Venice while Fielding was English ambassador there. Soon after their return to England, Rustat left Fielding's service for that of his relative, the young Duke of Buckingham. The Duke's father had been a favourite of Charles I and after he was murdered in 1628, the Duke was brought up with the King's own children.
Rustat remained devoted to Prince Charles, later Charles II, for the rest of his life; the diarist John Evelyn described him as "a very simple, ignorant, but honest and loyal creature". Once the Civil War broke out Rustat was one of the couriers of secret correspondence between the King in England and the Queen in Paris, and he served in the royal forces which were defeated at Kingston in July 1648. He joined Charles II in exile and soon became one of his favourite servants, accompanying him in his travels across France, Spain, Germany and the Netherlands. When the King had to leave France in 1654 Rustat was a middle ranking officer whose pay was fixed at 40 guilders a month. In 1659 he was summoned from Bruges to Bordeaux to bring the King clean clothes, and in the autumn of that year he was for a time the King's only attendant.
Appointed Yeoman of the Robes in 1659, much of Rustat's personal wealth came from this career as a courtier in the 1640s and afterwards. At this time, the line between public service and private enterprise was blurry, and courtiers holding office in the Royal household profited substantially from them.
His loyal service was well rewarded on the King's return to England. His wages as Yeoman of the Robes were doubled by the grant of an annuity, then doubled again by a 'customary allowance' for looking after the King's coronation and parliament robes, and yet again nearly doubled by his appointment as Under-Housekeeper at Hampton Court. All this came with free board, rent free official quarters, and a clothing allowance. Rustat remained Yeoman of the Robes until the death of Charles II in 1685, and retained his Hampton Court appointment until the arrival of William and Mary in 1689.
Rustat's wealth further increased when he became an investor in a series of trading companies: the Company of Royal Adventurers of England Trading into Africa, commonly called the Royal African Company, which was chartered in 1663 and reincorporated in 1672 as The Royal African Company (RAC). His name appears on the charter of both of these companies; he also seems to have been involved in another trading company, the Gambian Adventurers, into the early 1680s. Rustat's investment in the RAC was £400 (the equivalent of £60,000 today). Many courtiers and others in Rustat's circle invested, including the Duke of Buckingham and his private banker, Edward Backwell.
The RAC was not consistently profitable, but Rustat received significant dividends on his investment. He also took a role in running the RAC, being elected for a yearly term as an Assistant (the rough equivalent of a Director) in the years 1676, 1679, and 1680, although his direct involvement in the day-to-day management of the company was not great. Rustat thus had financial and other involvement in a slave trading company over a substantial period of time, including at the time when he donated to the College.
The historian William Pettigrew describes the activities of the RAC clearly: "The Royal African Company shipped more enslaved African women, men, and children to the Americas than any other single institution during the entire period of the transatlantic slave trade. From its foundation in 1672 to the early 1720s, the African Company transported close to 150,000 enslaved Africans, mostly to the British Caribbean." This was a brutal and sustained trade in human life that exploited thousands of people: investors in the RAC were fully aware of its activities and intended to profit from this exploitation.
Benefactions
Rustat's gift to Jesus, made in 1671, was part of a broader project of philanthropy that began in the mid–1660s.
Rustat's charitable giving had a political and religious agenda: as an ardent royalist during the Civil Wars, he intended his bequests primarily to support the established Church and the universities (which, like most early modern people, he would have understood as politicised, religious institutions), and to relieve clergy and their families who had suffered as a result of the religious and political upheavals of the 1650s.
Between 1665 and 1686 Rustat thus gave away more than £11,000 in support of the Church of England and his King, as well as to victims of misfortune, widows, the sick and elderly, and orphans. He contributed to the rebuilding of St Paul's Cathedral, and to the establishment of funds for clergy widows and for poor clergy in Leicestershire, for example.
His devotion to the Stuarts can be seen in the statues he commissioned of Charles II for Windsor Castle and Chelsea Hospital and of James II, now in Trafalgar Square. He also began a tradition of speeches at Jesus College, Cambridge and at St John's College, Oxford on the anniversaries of the battle of Edgehill, the execution of Charles I, and the restoration of Charles II.
In 1667 he gave Cambridge University Library its first endowment, to buy "the choicest and most useful" books. Four years later, in 1671, he made to Jesus, his father's College, his largest single gift (of £2,000) for scholarships for orphan sons of Anglican clergymen. Clergy orphans were a group that had been left especially vulnerable by the Civil War, as he knew all too well: one of his parson brothers had been evicted from his parish and had died before the Restoration, leaving three children for his widow to bring up. It may not be coincidence that 1671 was the same year in which his former employer, the Duke of Buckingham, was elected the University's Chancellor, and in which King Charles II paid his first visit to Cambridge.
His memorial
Rustat commissioned his own memorial from Grinling Gibbons, to be placed in Jesus College Chapel. He persuaded the great scholar and antiquary Sir William Dugdale to compose its memorable epitaph:
"Tobias Rustat, Yeoman of the Robes to King Charles the Second, whom he served with all duty and faithfullness, in his adversity as well as prosperity. The greatest part of the estate he gathered by God's blessing, the King's favour, and his industry, he disposed in his lifetme in workes of charity; and found the more he bestowed upon churches, hospitalls, universities and colleges and upon poor widows and orphans of orthodox ministers, the more he had at the year's end. Neither was he unmindful of his kindred & relation, in making them provisions out of what remained. He died a bachelour the 15th day of March, in the year of our Lord 1693. Aged 87 years."
At his death in 1694 (the epitaph's 1693 is Old Style) there was £4,000 and "a cellar of bottles", for his executors to distribute among his relatives, his godchildren, his friends, and his poor neighbours, as well as £2,000 for his great nephew.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: MYSQL reverse engineering EXTRACT() I had a date field like 2014-12-30 and I did something like EXTRACT(YEAR_MONTH FROM date) as yr_month which returned a value like 201412.
I'd like to retrieve the month from this new value, reverse engineer it back to the month. I tried MONTH(yr_mnth) and also MONTH(RIGHT(yr_mnth, 2)).
Any thoughts on how to retrieve the month from this field?

A: Try the following:
SELECT MONTHNAME(STR_TO_DATE(RIGHT(yr_mnth, 2), '%m'));

This will get you the monthname from the month number. As you can see here: MySQL MONTHNAME() from numbers

redpajama_set_name: RedPajamaStackExchange


Claudia Sagona, The Archaeology of Malta: From the Neolithic through the Roman Period. Cambridge world archaeology. New York: Cambridge University Press, 2015. Pp. xix, 449. ISBN 9781107006690. $135.00.
Part of the Cambridge World Archaeology series, this book presents a synthesis of the archaeology of Neolithic and Bronze Age Malta not achieved since John Evans's seminal Prehistoric Antiquities of the Maltese Islands,1 and in addition, also contains chapters covering Punic/Phoenician and Roman sites. This is an ambitious and compendious book that falls intermediately in scope and detail between Evans's work and Sagona's own treatise on Punic Malta,2 and more popular accounts of Maltese archaeology.3 The book is generally a success, but the treatment of various cultural phases is uneven and—especially in the Early Neolithic (Chapter 2) and Roman periods (Chapter 8)—the reader is left wondering whether more could have been made of the available evidence within Malta and its relationship to the surrounding Mediterranean worlds.
The book is structured chronologically, with each section containing a very useful overview of the material culture and evidence for the economy of each cultural phase that has been identified. Chapter 1 sets the scene: the Maltese Islands are a small archipelago in the central Mediterranean, comprised of limestone, isolated from neighbouring landmasses, and today rather denuded of natural vegetation. The islands are now intensively populated and perhaps were in the distant past too, as some of the world's most remarkable prehistoric sites are found in Malta and Gozo—most renowned of all are the megalithic buildings ('temples') built and used presumably for ritual between approximately 4000 and 2400 BC (i.e. the Copper Age, although due to an absence of the metal in Malta the term is not used). The book begins with a brief history of archaeology in Malta, explaining how international interest in these prehistoric monuments was first piqued in the 17th century. A number of antiquarian investigations followed, and by the late 19th century, the work had become recognisably 'archaeological'. By this time, the pace of development on Malta and Gozo had increased, and there was already an awareness that the archaeological resource was finite—indeed the richness of the islands' archaeology is a consequence of the successful management of this resource over years of rapid change. Twentieth-century archaeology in Malta saw the excavation of important prehistoric sites by Maltese, Italian and British workers, and from the 1960s onwards, the development of a radiocarbon-dated cultural sequence that provided and still provides a framework upon which the successive phases of prehistoric human occupation can be mapped.
All the available radiometric data for Malta is presented in Chapter 1 (with further details in an appendix) by graphing the probability distributions of the available dates. Missing is a some kind of way to summarise these data further; useful, for example, would have been a table of the various phases, associated with an estimated span of their dates. Although a map of sites mentioned in the text is given, there is no attempt to show the topography and geology of the islands, which are both important factors in the location of the sites, as discussed elsewhere in the book.
Chapter 2 provides an account of the evidence of the Ghar Dalam (5000-4300 BC), Grey Skorba (4500-4400 BC) and Red Skorba phases (4400-4100 BC), which aside from tenuous and inconclusive evidence for Palaeolithic, comprise the first, overlapping phases of Maltese prehistory. Much of the discussion is dependent on the finds, structures and samples from one site, also known as Skorba, where strata from most of the Maltese prehistoric phases were excavated between 1961 and 1963.4 Sagona points out both the similarity and growing differences between Maltese and Sicilian material culture during these times, although illustration of the Sicilian evidence would have been a useful addition for the reader. Also, there is no discussion about why the 'phases' or 'periods' (the terms are used interchangeably) apparently overlap during the 4400-4300 BC period; clearly there are very few radiocarbon dates to build the chronology upon, but the book does not adequately acknowledge the problem, let alone suggest ways in which the necessary refinements can be made.
Chapter 4 rehearses an important debate in Maltese archaeology concerning the end of the Late Neolithic period and what happened to the megalithic culture that reigned on the island for centuries but suddenly 'collapsed' or disappeared around 2400 BC.6 For Sagona, the period is a time of cultural transition rather than collapse or catastrophe, the presence of a unique 'Thermi ware' pottery type from Late Neolithic and Early Bronze Age strata at several sites providing evidence for a degree of continuity. But whether the period began after a break in the cultural sequence is a critical question that can only be answered, as Sagona points out (139), with more radiocarbon dates. The chapter also contains an interesting discussion on the origin of so-called 'cart ruts'—bedrock striations found in many parts of Malta—that Sagona proposes are somehow related to the act of artificially manufacturing soil to bring otherwise barren land into agricultural productivity. These features are very difficult to date, however, and indeed may be from slightly later in the Bronze Age. To fully accept Sagona's thesis, it will be necessary to compare their distribution not only to other archaeological sites (117) but also the bedrock geology and topographic factors, and unfortunately this exercise was not done.
The Early and Middle Bronze Ages in Malta are discussed in Chapter 5. Around 2400 BC, the dawn of the 'Tarxien Cemetery' period heralded new directions for the early Maltese, less apparent insularity and greater connection to other Mediterranean cultures, which are also themes that develop later in the book as the account of Punic and classical Malta plays out. This period differed from the Neolithic in terms of material culture, artistic style, and funerary rites. Next came the 'Borg in-Nadur period' beginning in 1500 BC (the transition is another archaeological mystery), which is described as a time of fortified sites at naturally strategic locations, small rural settlements, and caves used for dwelling and ritual.
The most satisfying Chapters in the book are 6 and 7, which concern the evidence for the Phoenician settlement of Malta in c. 750 BC (although there is much evidence for earlier influence), and their descendants who, two centuries later, came to be known by their Roman competitors as Punic. The end of the Bronze Age is considered first (in Chapter 6), the timing of which is again bedevilled by poor dating. Here, and elsewhere in the book, Sagona is keen to stress continuity, although given the absence of archaeological evidence, one is left wondering what the reasons for this are.
Reading Chapter 8 'Malta's Place in the Roman World', one is left with the impression that the archaeological evidence for this period is slight. This may be somewhat misrepresentative of the reality—there are many sites, although few that approach the magnificent vestiges of the Roman world found elsewhere in the Mediterranean. There was apparently much continuity with Punic lifeways, the best archaeological evidence for which is present in the funerary record, but unfortunately discussion on this important theme is limited to less than two pages, and fails to account for how burial practices evolved and were elaborated throughout the Roman period.
Some aspects of Sagona's book must be highly praised: there are detailed drawings and even some photographs of the bewildering array of pottery styles from each cultural phase. The appendices are excellent and very useful, giving a complete and georeferenced list of archaeological sites from the periods in question and details of radiocarbon dates. The bibliography too is a goldmine of information, and will be useful for years to come as a guide to the quite sizable body of literature concerning Malta and its archaeological past. There is an excellent index. However, for a volume that aspires to 'The Archaeology of Malta', there are too many frustrating omissions in the discussions, and simultaneously too much entanglement of fact and interpretation. That said, the book is a useful contribution, and as such the publisher ought to have provided better proofing, thicker paper and the perhaps some colour photographs—considering that the book is lacking in all these respects, its fairly high cost is difficult to justify.
1. Evans, J. D. 1971. The prehistoric antiquities of the Maltese Islands. (London: Athlone Press).
2. Sagona, C. 2002. The Archaeology of Punic Malta. (Leuven: Peeters Press).
3. Given by, for example, Trump, D. H. 1990. Malta: An archaeological Guide. (Valletta: Progress Press Co. Ltd.; and Cilia, D. 2004). Malta Before History. (Malta: Miranda).
4. Trump, D. H. 1966. Skorba. (London: Society of Antiquaries).
5. E.g. Evans 1971, 116-149.
6. See Malone et al. (ed.) 2009. Mortuary Customs in prehistoric Malta. (Cambridge: McDonald Institute for Archaeological Research), pp. 341-346.


text:
Tag Archives: Russian military
The Real Reason Why Putin Pulled Out of Syria
By Joshua Krause – The Daily Sheeple In the West, most intel analysts have a habit of saying that Putin is dangerous and unpredictable. He always seems to be one step ahead of the US, and his actions often come as a complete surprise to Western governments. He keeps his cards close, and allegedly, even many…
March 17, 2016 in News/ Current Events, War.
Putin Is Pulling Russia Out Of The Syrian War
In a sudden and surprising move, President Vladimir Putin announced that Russia will be pulling their troops out of the five-year-old Syrian civil war. In a move that supposedly has Assad's approval, Russian troops will be pulled back to a seaport and airbase in the Syrian province of Latakia. This effectively removes them from the…
March 16, 2016 in Civil Unrest, War.
Putin to Withdraw Russian Forces in Syria: "Objectives Achieved… Terrorists Have Been Forced Out"
By Mac Slavo – SHTFplan.com Now it is Putin's "Mission Accomplished" moment in the Middle East. Who can say what Putin's long term objectives are, but regardless, he has won this round. His decision to bring Russian troops into Syria has weakened ISIS, secured Assad's regime (for now) and put American aggression into check. Perhaps…
Russia Threatens N Korea With Invasion Over Nuclear Threats
By Joshua Krause – The Daily Sheeple By now, most people are used to North Korea's antics and provocations. Despite the fact that they have a massive ground army and nuclear weapons, it's difficult to take their threats seriously anymore. Their latest transgressions involve threatening a preemptive nuclear strike in response to joint US and South…
March 11, 2016 in War.
WW3 Ahead? Pentagon to Quadruple Military Budget for European Theater
By Joshua Krause – The Daily Sheeple The way Washington and the military establishment have been talking these days, you'd think that the Russians are going to be paradropping onto our high schools any day now. Ever since Russia put its foot down over Syria, our government has taken a hard stance against them, and you…
February 5, 2016 in War.
Why the U.S. Should Be Worried About the Russian Navy
By Joshua Krause – The Daily Sheeple I don't think anyone doubts that the US Navy is the most formidable naval force in the world. It is the one asset that allows Washington to project its conventional military power to almost any location on the planet, and often at a moments notice. Without it, our…
January 4, 2016 in News/ Current Events, War.


text:
Johannesburg Upgraded
Now it is official. Mukhanyo's big Johannesburg branch has recently been approved by the Department of Higher Education (DHET) as a campus. Until this point it was only an advanced distance learning centre.
The Reformed Church of Johannesburg was discontinued in 2002 and the building was then donated to Mukhanyo who has been using it as a distance learning centre for a number of years. Thanks to some substantial donations, it was upgraded in 2018 with a library and several additional lecture rooms and offices. The number of students has increased to some 30 students who have been using the facilities and receiving instruction.
The new status as campus will enable the continuation of the growth in the number of students, also enabling them to be full-time students with contact lectures weekly from Tuesdays to Thursdays. Most students are involved with church work at their place of residence over weekends.
These campus facilities are located in the heart of Johannesburg and close to train and bus stations. The renovated building has the capacity to facilitate far more students than the present – more than 100 full-time and part-time students. Let's pray that the status as a full-grown campus will draw in an increasing number of students.


Three months have passed since we stepped into the Pacific Ocean. We have been settling back into the domestic way of life, trying to wrap our heads around organizing our house ("what is all this stuff?") and building a daily routine that isn't maddening. Things have been weird. Now that I have gotten my bearings and can form coherent sentences again, we'll pick the story up after we left San Francisco, and started to drive our way back East.
We took our time driving back across the Great American Bulge. Unnerved by the isolation and intense speed of the Interstates, we abandoned those straightest lines for roads less traveled across the deserts of Arizona and New Mexico. Whenever possible, we sought quieter, rougher roads with faded lines and sweeping vista. We stayed with Kait's aunt and uncle Lynn and Alvaro outside of Tucson. Their house sits up on a hill or mountain of some kind, overlooking the valley. We watched the sun set and the city glow and just out of sight on the horizon was Mexico.
Our stop was brief and refreshing, and we were on the road again. Outside of Safford, Arizona, we pulled onto a rough dirt road to find a place to camp for our last night under the desert stars. We had planned it out ahead of time and picked up groceries and drinks for the occasion; after all, we had grown quite attached to the desert's awe-inspiring openness, and this would be our last time sleeping under its unabashedly clear night sky.
We found an unmarked ranch road and followed it back about a quarter of a mile until we came to a flat elevated clearing, where we pitched our tent and cooked our dinner over the fire while the stars whirled infinite above our heads.
The next morning, the car wouldn't start – dead battery. We sat and scratched our heads and thought. We could walk back out to the small highway we had turned off of, and try to flag down a brave-looking driver to follow us back up the ragged dirt road and give us a jump; or we could keep walking down the dirt road and see what we found at the end. We looked at the map and decided to walk the mile or so further back to see if there was a house or ranch at the end of the drive.
It is hard to say what you might find at the end of an unmarked dirt road in the middle of the desert. On that day, we were lucky enough to find Cold Creek Ranch and the Schwennesen family. I knocked on the front door and Jean answered, and immediately offered to help when I explained our situation. She didn't even bat an eyelash at the fact that we had camped on their property, and they were happy to give us a lift back to the car and give us a jump. It's good to know our trail magic didn't end at the ocean.
Back on the road, we backtracked to Safford to buy a new car battery. Better safe than sorry, as they say. And onward we drove to Albuquerque, to see our best friend Scott. He's just the best. We spent a brief night with him and his wife, Tracy, catching up and cackling on about all sorts of ridiculous and paranoid delusions. Scott and I just get each other. In the morning we headed off for Santa Fe to stay with Kait's cousin Alison. Scott promised to head up for a day or two, and we planned to hang out for several nights. Alison and Molly live up on a hill, and their yard backs up to a sprawling dog park that overlooks the city.
Santa Fe was a blur. We planned to see the city, but our first day in town it snowed all day. In the desert. The flakes came down heavy and clung to adobe walls and cactus thorns.
The snow melted off and Ali drove us out to hike Tsankawi Mesa, an annex of Bandalier National Monument. The short loop trail followed ancient paths worn deep into the soft rocks, past ruins littered with pottery fragments and caves with ancient paint chipping from the walls.
After our little field trip back in time, we cut hard north through Kansas, heading towards Kait's brother up in Milwaukee. We stayed a night in Dodge City because, well, it's Dodge City, and the next day found ourselves retracing some of our steps in southern Nebraska. A certain stretch of U.S. Route 6 – between Minden and Holdrege – stuck out as a particularly amazing day of walking. We drove it in about 20 minutes. Unreal.
Once again, we stayed the night with the O'Brient family in Villisca, Iowa. Staying with Jesse and Krystal again was a milestone in our re-entry, because knowing that they were real, living in their house just like we remembered, with their charming boys and deer skulls in the garage – it made the whole Walk real again. Staying with a host family from the Walk reminded us that it wasn't just a dream – we were here. We knew that place, and those people, and they are still real and wonderful. That night, lying on the couch in their living room, I quietly wept in the comfort of their home.
And we made one final push north, through Chicago toward Milwaukee. We caught Tyler for lunch, and confirmed that we are indeed a rare breed of lunatic to have done what we did. Tyler even made bumper stickers that he only gives to other people who have walked coast-to-coast. He's a fun guy. And we belong to a very strange and exclusive club of transcontinental pilgrims, and dedicated seekers of all things unsought.
Milwaukee was our last stop before heading back to Kentucky to unpack and breathe through the panic of "settling down". Patrick, Kait's younger brother, took us out on the town and laughed at our oddities. We are like wild beasts, trying to learn how to live inside and use toilets again. One day at a time, as they say. One day at a time.
We said our goodbyes and dropped south and east toward my old Kentucky stomping grounds. Just before we made it home, we stopped to find a fellow Walker out on the roads of Indiana. Jonah is fresh out of high school and taking time off before college to walk across America. He is chronicling his coast-to-coast adventure at Dudetrek.com. His plan was to walk on into the winter for as long as he could, and hunker down if he got to the Rockies before the spring thaw. That is one ballsy plan, man.
We took him out to dinner and talked his ear off, before dumping him on the side of the road, in the dark. We had a moment of realization when we found ourselves worried about his safety. Suddenly, we understood all of the hosts and people we met on the road that worried for us, and didn't want us to leave, and went out of their way to check up on us. Coming from the walk, we knew exactly the kind of dangers Jonah is facing – and I don't know if we worried more or less because of it.
So if you see Jonah (I think he's in Missouri as of this posting,) buy him a big hot meal if you can. And ask him to play you a song. And tell him we sent you.
This entry was posted in Re-Entry Syndrome, Trail Life and tagged across, america, arizona, bandalier, dog, dude trek, dudetrek, national monument, new mexico, pueblo, road, santa fe, trip, tsankawi, tucson, walk. Bookmark the permalink.
Thank you for your honesty, it makes me a bit misty. I hope you continue to settle back into "normal" life, but still find time to escape to the wild places. Pretty sure you will.
Please take care and I hope to meet you all sometime in near future.
p.s. I bookmarked Jonah's blog and will follow his adventures now!
Ditto what Mary Lou said! Cannot wait for the book. I cannot really imagine how odd it must feel to go from 8 1/2 months of relative solitude, adventure dirt and unknown on the walk, to the comforts and boring stability of "normal" life back in Louisville. Whenever I have travelled, no matter how great a time I had, it was always a comforting feeling to be HOME again. Back to my chair, my clothes, my bed, my pets (you did have your dogs with you) my routine, my things. I suppose after so long without such things the feeling is a bit different, as if it is all a foreign thing, another life you have almost forgotten. Or, is it as if you never left, an even more odd feeling? Much love. You two have proven you can accomplish anything-given enough courage and ambition, good luck with this next phase of your lives.
give jonah my # if you like and when he passes this way, i will host him.


text:
\section{Introduction}
\vspace{-5pt}

Person re-identification (re-ID) aims at identifying images of the same person across multiple cameras.
Despite great advances of deep learning-based re-ID methods,
large domain gaps still pose great challenges on generalizing the trained models from a labeled source domain to an unlabeled target domain.
There are two main research directions towards solving the problem of domain adaptive person re-ID, \textit{i}.\textit{e}.,  domain translation-based methods \cite{deng2018image,wei2018person,deng2018similarity,chen2019instance} and pseudo-label-based methods \cite{fan2018unsupervised,zhang2019self,yang2019selfsimilarity,ge2020mutual},
where the latter ones dominate the current literature with state-of-the-art performance.

Although domain translation-based methods have fallen out of favor in recent years due to their uncompetitive performance,
we argue that they have great potential to make use of valuable source-domain data with accurate identities.
Translating source-domain images to the target domain to create new training samples with identity labels is at the core of domain translation-based methods.
Previous works used identity-based regularization (\textit{e}.\textit{g}.,  classification loss \cite{deng2018similarity,chen2019instance} or contrastive loss \cite{deng2018image}) to preserve the ID-related appearance during translation.
However, we observe that their domain-translated images cannot well maintain \emph{inter-sample relations} even with such ID-based constraints (Figure \ref{fig:fig1}(a)).
We argue that such relation consistency is critical for generating informative training samples as they better capture distributions of source-domain data.

\begin{figure}
\begin{center}
    \scriptsize
    \begin{tabular}{c@{}}
   \includegraphics[width=0.95\linewidth]{fig1_tab.pdf} \\
     \begin{tabular}{c@{}c@{}}
      \includegraphics[width=0.45\linewidth]{fig1a.pdf} ~~~ & ~~~ \includegraphics[width=0.45\linewidth]{fig1b.pdf} \\
  (a) Existing domain translation-based UDA methods \cite{deng2018image,wei2018person,deng2018similarity,chen2019instance}. ~~~ & ~~~
  (b) Our proposed structured domain translation.
    \end{tabular}
   \vspace{2pt} \\
    \begin{tabular}{c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c@{\hspace{1.5mm}}c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c@{\hspace{1.5mm}}c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c@{\hspace{1.5mm}}c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c|c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c@{\hspace{1.5mm}}c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c@{\hspace{1.5mm}}c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c@{\hspace{1.5mm}}c@{\hspace{0.5mm}}c@{\hspace{0.5mm}}c}
   	\multicolumn{3}{c}{{\tiny Two IDs}} & \multicolumn{3}{c}{{\tiny Look similar}} & \multicolumn{3}{c}{{\tiny Look similar}} & \multicolumn{3}{c}{{\tiny Keep difference}} & \multicolumn{3}{c}{{\tiny Two IDs}} & \multicolumn{3}{c}{{\tiny Look different}} & \multicolumn{3}{c}{{\tiny Look different}} & \multicolumn{3}{c}{{\tiny Keep similarity}} \\
        \includegraphics[scale=0.1]{imgs//plot//source//0331_c1s2_002991_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//source//0331_c1s2_015441_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//source//0427_c4s2_032217_01.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//cycle//0331_c1s2_002991_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//cycle//0331_c1s2_015441_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//cycle//0427_c4s2_032217_01.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//spgan//0331_c1s2_002991_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//spgan//0331_c1s2_015441_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//spgan//0427_c4s2_032217_01.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//ours//0331_c1s2_002991_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//ours//0331_c1s2_015441_02.jpg} &
        \includegraphics[scale=0.1]{imgs//plot//ours//0427_c4s2_032217_01.jpg} &

        \includegraphics[scale=0.1]{imgs//duke2market//Source//0196_c1_f0088033.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//Source//0196_c1_f0088273.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//Source//0082_c7_f0049684.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//Cyclegan//0196_c1_f0088033.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//Cyclegan//0196_c1_f0088273.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//Cyclegan//0082_c7_f0049684.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//SPGAN//0196_c1_f0088033.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//SPGAN//0196_c1_f0088273.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//SPGAN//0082_c7_f0049684.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//SDA//0196_c1_f0088033.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//SDA//0196_c1_f0088273.jpg} &
        \includegraphics[scale=0.1]{imgs//duke2market//SDA//0082_c7_f0049684.jpg} \vspace{-2pt} \\

         \multicolumn{3}{c}{\tiny Source images} & \multicolumn{3}{c}{\tiny CycleGAN} & \multicolumn{3}{c}{\tiny SPGAN} & \multicolumn{3}{c}{\tiny Ours} & \multicolumn{3}{c}{\tiny Source images} & \multicolumn{3}{c}{\tiny CycleGAN} & \multicolumn{3}{c}{\tiny SPGAN} & \multicolumn{3}{c}{\tiny Ours} \\
    \end{tabular} \vspace{2pt}\\

    (c) Domain-translated triplets by CycleGAN \cite{zhu2017unpaired}, SPGAN \cite{wei2018person} and our structured domain-translation method.
    \end{tabular}
    \vspace{-8pt}
    \caption{
(a)  The original inter-sample relations of source-to-target translated images are not maintained by existing domain translation-based methods. (b) Our proposed online relation regularization better preserves the inter-sample relations for improving cross-domain person re-ID. (c) Example triplets by SPGAN \cite{deng2018image} and our proposed method. Best viewed in color. \vspace{-5pt}}

    \label{fig:fig1}
\end{center}
\end{figure}

To tackle this challenge, we propose an end-to-end structured domain adaptation (SDA) framework with a novel \emph{online relation-consistency regularization} term.
It consists of a structured domain-translation network, a source-domain person image encoder and a pseudo-label-based target-domain encoder.
The two domain encoders are coupledly trained to model inter-sample relations on-the-fly for regularizing domain translation, which in turn, boosts the target-domain encoder and its generated pseudo labels with informative source-to-target translated images.
The structured domain-translation network adopts the CycleGAN \cite{zhu2017unpaired} architecture for translating source- and target-domain images.
A novel relation-consistency loss is proposed to regularize the training of source-to-target domain translation for maintaining the original inter-sample relations, which are generated by the source-domain encoder on-the-fly (Figure \ref{fig:fig1}(b)).
Our relation-consistency loss is different from conventional ID-based constraints in \cite{deng2018image,wei2018person,deng2018similarity,chen2019instance}, which only apply \emph{constant} regularization that requires translated images of different classes to be well separated after translation.

The source-domain encoder is trained with source-domain images and ground-truth identifies.
An improved target-domain encoder is trained with both the source-to-target translated images and target-domain images via a joint cross-domain label system, which is constructed with their associated ground-truth and pseudo labels.
In this way, both the target-domain encoder and its generated pseudo labels can be improved with the optimization of the structured domain-translation network.

The contributions of this paper could be summarized as three-fold.
(1) To properly exploit the valuable source-domain data
in domain translation-based UDA methods,
we propose a novel online relation-consistency regularization term to better supervise the translation process.
(2) The domain-translated images can serve as informative training samples to improve the target-domain encoder and help generate more accurate pseudo labels.
The domain translation network and target-domain encoder alternately promote each other to achieve optimal re-ID performance.
(3)
Our framework achieves state-of-the-art performance on multiple domain adaptive person re-ID benchmarks.

\vspace{-5pt}
\section{Related Work}
\vspace{-5pt}

\paragraph{Domain translation-based UDA methods for person re-ID.}

Domain translation-based methods \cite{deng2018image,wei2018person,deng2018similarity,chen2019instance} aimed at fine-tuning the target-domain re-ID model with source-to-target translated images and their ground-truth identities.
In order to preserve the original identities of the translated images,
ID-based regularizations were adopted on either pixel level \cite{wei2018person} or feature level \cite{deng2018image,deng2018similarity,chen2019instance} via the contrastive loss \cite{deng2018image} or classification loss \cite{deng2018similarity,chen2019instance}.
However, we found that they are too weak to properly maintain the original inter-sample relations and distributions of source-domain data during translation, which are critical for generating informative training samples.

\vspace{-5pt}
\paragraph{Pseudo-label-based UDA methods for person re-ID.}

Pseudo-label-based methods \cite{fan2018unsupervised,zhang2019self,yang2019selfsimilarity,ge2020mutual,yu2019unsupervised,zhong2019invariance}
achieved state-of-the-art performances by modeling relations between unlabeled target-domain data with generated pseudo labels, where a clustering-based pipeline was found effective. PUL \cite{fan2018unsupervised} first proposed a self-training scheme with clustering labels. SSG \cite{yang2019selfsimilarity}, PAST \cite{zhang2019self} and MMT \cite{ge2020mutual} further extended this type of methods by introducing human part features, progressive training strategy and mutual learning.
These methods generally focused on using only the target-domain data, and we found that they could be further improved by properly exploiting the valuable source-domain data with ground-truth identities, where domain translation has great potential.

\vspace{-5pt}
\paragraph{Generic UDA methods.}

Feature-level and pixel-level adaptations were commonly adopted by UDA methods for tackling more general tasks.
The feature-level adaptation methods \cite{long2015learning,sun2016deep,tzeng2017adversarial,das2018unsupervised,das2018sample} aimed at aligning the feature distributions between the source and target domains by learning domain-invariant features with a domain adversarial discriminator \cite{bousmalis2016domain,tzeng2017adversarial} or reducing the Maximum Mean Discrepancy (MMD) \cite{gretton2007kernel} distance between domains.
However,
such methods are unable to handle the open-set re-ID problem with disjoint label systems in two domains \cite{zhong2019invariance,panareda2017open,saito2018open}.
The other category of pixel-level adaptation methods \cite{hoffman2017cycada,li2019bidirectional,chen2019crdoco} minimized the domain shifts by translating images to the same domain,
which has been widely studied in semantic segmentation.
However,
existing pixel-level adaptation methods still ignored the consistency of inter-sample relations during translation, facing the same challenge as translated-based UDA methods for person re-ID \cite{deng2018image,wei2018person,deng2018similarity,chen2019instance}.

\vspace{-5pt}
\section{Structured Domain Adaptation for Unsupervised Person Re-ID}
\vspace{-5pt}

\begin{figure}[t]
\centering
\includegraphics[width=0.7\linewidth]{fm1_new.pdf}
\vspace{-5pt}
\caption{Illustration of our structured domain adaptation (SDA) framework and the novel online relation-consistency regularization term.
The domain-translation network and the target-domain encoder alternately promote each other via joint training to achieve optimal re-ID performance. \vspace{-5pt}}
\label{fig:fm}
\end{figure}

We propose a structured domain adaptation framework with a novel online relation-consistency regularization term to tackle unsupervised domain adaptation (UDA) for person re-ID. The overall framework, as illustrated in Figure \ref{fig:fm}, consists of a structured domain-translation network and two domain-specific person image encoders, which are jointly optimized and promote each other to learn more discriminative person features.

The key innovation of our framework lies in the generation of informative training samples by translating source-domain images into the target domain under the relation-consistency regularization generated by the image encoders on-the-fly.

\vspace{-5pt}
\subsection{Source-domain Encoder Pre-training}
\label{sec:reid}
\vspace{-5pt}

We pre-train the source-domain person image encoder $\mathcal{F}^s$
for (1) providing ``ground-truth'' inter-sample relations between source-domain images to regularize the proposed structured domain translation, and
(2) providing weight initialization for the target-domain person image encoder $\mathcal{F}^t$.
Once trained, $\mathcal{F}^s$ is frozen to provide stable regularizations for inter-sample relations.

Given source-domain samples ${\mathbb{X}}^s$, the encoder $\mathcal{F}^s$ is trained to transform each sample $x^s \in {\mathbb{X}}^s$ into a feature vector ${\bm{f}}^s=\mathcal{F}^s(x^s)$.
If the feature vector ${\bm{f}}^s$ is properly embedded, it could be used to correctly predict its ground-truth identity $y^s$ with a learnable classifier $\mathcal{C}^s: {\bm{f}}^s \to \{1,\cdots,p^s \}$, where $p^s$ is the number of identities in the source domain.
A cross-entropy classification loss $\ell_{ce}$ and a triplet loss \cite{hermans2017defense} are adopted jointly for training,
{\small
\begin{align}
\label{eq:source}
&\mathcal{L}_\text{enc}^s(\mathcal{F}^s,\mathcal{C}^s) =
\mathbb{E}_{x^s \sim {\mathbb{X}}^s}\left[ \ell_\text{ce} ( \mathcal{C}^s({\bm{f}}^s), y^s  )\right] +
\mathbb{E}_{x^s \sim {\mathbb{X}}^s}\left[
(\|{\bm{f}}^s-{\bm{f}}^s_p\| + m
 - \|{\bm{f}}^s-{\bm{f}}^s_n\| )^+\right],
\end{align}}%
where
$(\cdot)^+=\max(0,\cdot)$ with a margin $m$, and the subscripts $_p, _n$ denote the mini-batch's hardest positive and negative feature indexes for the anchor ${\bm{f}}^s$.

\vspace{-5pt}
\subsection{Structured Domain Translation with Online Relation Regularization}
\label{sec:sdt}
\vspace{-5pt}

We propose a structured domain-translation (SDT) network to generate informative training samples by translating source-domain images ${\mathbb{X}}^s$ to the target domain, which focuses not only on image-style transfer but more on how to maintain their original inter-sample relations.
We adopt the widely-used CycleGAN~\cite{zhu2017unpaired} architecture for our translation network, which is trained to translate images along two directions
with corresponding generators $\mathcal{G}^{s\to t}$ and $\mathcal{G}^{t\to s}$.

\vspace{-5pt}
\paragraph{Conventional cycle generation losses.}
The general training objective of a CycleGAN \cite{zhu2017unpaired} for image-to-image translation consists of the adversarial losses $\mathcal{L}_\text{adv}^s$, $\mathcal{L}_\text{adv}^t$, the cyclic reconstruction loss $\mathcal{L}_\text{cyc}$ and the appearance consistency loss $\mathcal{L}_\text{apr}$. We adopt the loss function of LSGAN \cite{mao2017least} with two domain discriminators $\mathcal{D}^s$ and $\mathcal{D}^t$ as
{\small
\begin{align}
\mathcal{L}_\text{adv}^s&(\mathcal{G}^{t\to s},\mathcal{D}^s)=\mathbb{E}_{x^s\sim {\mathbb{X}}^s}\left[\mathcal{D}^s(x^s)^2 \right] +\mathbb{E}_{x^t\sim {\mathbb{X}}^t}\left[\left(\mathcal{D}^s(\mathcal{G}^{t\to s}(x^t))-1 \right)^2 \right],
\nonumber \\
\mathcal{L}_\text{adv}^t&(\mathcal{G}^{s\to t},\mathcal{D}^t)=\mathbb{E}_{x^t\sim {\mathbb{X}}^t}\left[\mathcal{D}^t(x^t)^2 \right] +\mathbb{E}_{x^s\sim {\mathbb{X}}^s}\left[\left(\mathcal{D}^t(\mathcal{G}^{s\to t}(x^s))-1 \right)^2 \right].
\end{align}}%
The cyclic reconstruction loss supervises the pixel-level generation by translating the images twice,
{\small
\begin{align}
\mathcal{L}_\text{cyc}(\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s}) =& \mathbb{E}_{x^s\sim {\mathbb{X}}^s}\left[\| \mathcal{G}^{t\to s}(\mathcal{G}^{s\to t}(x^s)) - x^s \|_1 \right]
+\mathbb{E}_{x^t\sim {\mathbb{X}}^t}\left[\| \mathcal{G}^{s\to t}(\mathcal{G}^{t\to s}(x^t)) - x^t \|_1 \right] .
\end{align}}%
The appearance consistency loss \cite{taigman2017unsupervised} maintains the general color composition after translation,
{\small
\begin{align}
\mathcal{L}_\text{apr}(\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s})=&\mathbb{E}_{x^s\sim {\mathbb{X}}^s}\left[\| \mathcal{G}^{t\to s}(x^s) - x^s \|_1 \right] +
\mathbb{E}_{x^t\sim {\mathbb{X}}^t}\left[\| \mathcal{G}^{s\to t}(x^t) - x^t \|_1 \right].
\end{align}}%
Despite the fact that the above loss terms guide the source-domain images to have target-domain image style,
the generated images generally fail to maintain their original inter-sample relations and are therefore not accurate enough to optimize the target-domain image encoder.

\vspace{-5pt}
\paragraph{Online relation-consistency loss.}
The appearance consistency loss $\mathcal{L}_\text{apr}$ alone is not enough to maintain accurate inter-sample relations during translation,
we propose to use the pre-trained $\mathcal{F}^s$ to provide relation supervision on-the-fly for regularizing the translation.
Intuitively, inter-sample relations can be modeled by the ratio of feature similarities between pairs of images.
In person re-ID, triplets with intra-/inter-identity samples are generally most representative and can be used for modeling better inter-sample relations.

Given a source-domain image $x^s$, its positive sample $x^s_p$ with the same identity, and its negative sample $x^s_n$ with a different identity,
we can measure their similarity-based inter-sample relations on-the-fly among the triplet with a softmax-like function,
{\small
\begin{align}
\mathcal{R}(x^s; \mathcal{F}^s)=\frac{\exp \langle {\bm{f}}^s, {\bm{f}}^s_p\rangle}
{\exp \langle {\bm{f}}^s, {\bm{f}}^s_p\rangle + \exp \langle {\bm{f}}^s, {\bm{f}}^s_n \rangle} \in [0,1],
\label{eq:rel_s}
\end{align}}%
where ${\bm{f}}^s$, ${\bm{f}}_p^s$, ${\bm{f}}_n^s$ are the features encoded by the pre-trained source-domain encoder $\mathcal{F}^s$ on the image samples $x^s$, $x_p^s$, $x_n^s$, respectively, and $\langle \cdot, \cdot \rangle$ is the inner product between two feature vectors to measure their similarity.
Similar to \cite{hermans2017defense}, we utilize only the most difficult triplet of each sample $x^s$ within a batch, \textit{i}.\textit{e}.,  the hardest positive (${\bm{f}}^s_p$) and negative (${\bm{f}}^s_n$) samples for each ${\bm{f}}^s$. Note that $\mathcal{R}(x^s; \mathcal{F}^s)$ is a \emph{continuous} value in $[0,1]$ to measure the ratio of pairwise similarities.

After translating source-domain images to the target domain by $\mathcal{G}^{s \to t}$, we obtain the features of the source-to-target translated triplet
(${{\bm{f}}}^{s \to t}, {{\bm{f}}}^{s \to t}_p, {{\bm{f}}}^{s \to t}_n$), which are encoded by the target-domain encoder $\mathcal{F}^t$ (to be discussed in Section \ref{sec:joint}).
Similarly, the continuous similarity ratio in $[0,1]$ between the translated images can also be measured by a softmax-like function as
{\small
\begin{align}
\mathcal{R}(x^s&; \mathcal{G}^{s \to t}, \mathcal{F}^t) = \frac{\exp \langle {{\bm{f}}}^{s \to t}, {{\bm{f}}}^{s \to t}_p \rangle}
{\exp \langle {{\bm{f}}}^{s \to t}, {{\bm{f}}}^{s \to t}_p\rangle  + \exp \langle {{\bm{f}}}^{s \to t}, {{\bm{f}}}^{s \to t}_n\rangle} \in [0,1].
\label{eq:rel_T}
\end{align}}%
We claim that, if the domain-translation network well preserves the source-domain images' inter-sample relations, their softmax-triplet responses in $[0,1]$ should be similar.
Based on this assumption, a novel relation-consistency loss is introduced to regularize the inter-sample relations after the translation $s\to t$ by a ``soft'' binary cross-entropy loss as
{\small
\begin{align}
\label{eq:sc}
\mathcal{L}_\text{rc}(\mathcal{G}^{s\to t})=
\mathbb{E}_{x^s\sim {\mathbb{X}}^s} \left[ \ell_\text{bce} \left( \mathcal{R}(x^s; \mathcal{G}^{s \to t}, \mathcal{F}^t),  \mathcal{R}(x^s; \mathcal{F}^s) \right) \right],
\end{align}}%
where $\ell_\text{bce}(p, q) = - q \log p - \left(1- q\right) \log \left( 1-p\right)$ with the soft label $q$.
We use the source-domain inter-sample relations $\mathcal{R}(x^s; \mathcal{F}^s)$ as \emph{soft} learning targets for supervising the translated inter-sample relations $\mathcal{R}(x^s; \mathcal{G}^{s \to t}, \mathcal{F}^t)$.

\vspace{-5pt}
\paragraph{Differences with existing ID-based regularizations.}
There are two key differences between the proposed relation-consistency loss and existing ID-based regularizations, \textit{e}.\textit{g}.,  classification loss \cite{deng2018similarity,chen2019instance}, contrastive loss \cite{deng2018image} or triplet loss.
(1) Existing ID-based regularizations only require the samples from different classes to be well separated after translation, which is too weak to maintain inter-sample relations. As long as the samples are classified correctly, even if they did not maintain inter-sample relations well, they receive little penalty. In contrast, our proposed regularization tries to maintain continuous and more sensitive relation measurements (Eq. (\ref{eq:rel_s})) during domain translation.
(2) Existing regularizations utilize static learning targets (identity labels), while our proposed term generates relation measurements with image encoders on-the-fly to provide adaptive supervisions.
In other words, previous ones only acquire knowledge from ground-truth labels, while ours exploits abundant knowledge from both ground-truth labels and the pre-trained source-domain encoder.

There exist some works \cite{liu2018pose,ge2018fd,zheng2019joint} which leveraged generative models on fully-supervised person re-ID tasks. They focused on preserving person identities with existing ID-based regularizations, which are too weak to maintain inter-sample relations as discussed above.

\vspace{-5pt}
\paragraph{Joint training objective.}
During training, we fix $\mathcal{F}^s$ and alternately update $\mathcal{F}^t$ and SDT in each iteration to avoid bias amplification,
where the SDT network is optimized with
{\small
\begin{align}
\mathcal{L}_\text{sdt}(\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s}, \mathcal{D}^s, \mathcal{D}^t)=&
\lambda_\text{rc}\mathcal{L}_\text{rc}(\mathcal{G}^{s\to t})
+\lambda_\text{cyc}\mathcal{L}_\text{cyc}(\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s})
\nonumber\\
&+\lambda_\text{adv}\left(\mathcal{L}_\text{adv}^s(\mathcal{G}^{t\to s}, \mathcal{D}^s)+\mathcal{L}_\text{adv}^t(\mathcal{G}^{s\to t}, \mathcal{D}^t)\right)
+\lambda_\text{apr}\mathcal{L}_\text{apr}(\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s}).
\label{eq:sdt}
\end{align}}%
Here $\lambda_\text{rc}$, $\lambda_\text{cyc}$, $\lambda_\text{adv}$ and $\lambda_\text{apr}$ are the weighting factors for different loss terms.

\vspace{-5pt}
\subsection{Pseudo-label-based Target-domain Encoder with Translated Images}
\label{sec:joint}
\vspace{-5pt}

\begin{algorithm}[tb]
\caption{\small Structured domain adaptation for unsupervised person re-ID}
\label{overallalg}
\scriptsize
\begin{algorithmic}[1]
\REQUIRE Labeled source-domain data ${\mathbb{X}}^s$, unlabeled target-domain data ${\mathbb{X}}^t$;
\REQUIRE Weighting factors $\lambda_\text{rc},\lambda_\text{cyc},\lambda_\text{adv},\lambda_\text{apr}$ for Eq. (\ref{eq:sdt});
\STATE Pre-train source-domain encoder $\mathcal{F}^s$ by minimizing Eq. (\ref{eq:source}) on ${\mathbb{X}}^s$;

\FOR{$n$ in $[1, \text{num\_epochs}]$}
\STATE Create pseudo labels by clustering $\mathcal{F}^t({\mathbb{X}}^t)$;
\FOR{each mini-batch ${\mathbb{B}}^s \subset {\mathbb{X}}^s$, ${\mathbb{B}}^t \subset {\mathbb{X}}^t$}
\STATE Translate ${\mathbb{B}}^s$ into the target domain as ${\mathbb{B}}^{s\to t}$ by $\mathcal{G}^{s\to t}$; \
\STATE Update $\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s}$ by minimizing the objective function Eq. (\ref{eq:sdt}) with $\mathcal{D}^s, \mathcal{D}^t$ fixed, where the inter-sample relations are measured by $\mathcal{F}^s$ and $\mathcal{F}^t$ on-the-fly; \
\STATE Update $\mathcal{F}^t$ by minimizing the objective function Eq. (\ref{eq:pic}) with ${\mathbb{B}}^{s\to t} \cup {\mathbb{B}}^t$; \
\STATE Update $\mathcal{D}^s, \mathcal{D}^t$ by maximizing the objective function Eq. (\ref{eq:sdt}) with $\mathcal{G}^{s\to t}, \mathcal{G}^{t\to s}$ fixed. \
\ENDFOR
\ENDFOR
\end{algorithmic}
\end{algorithm}

For training the target-domain encoder $\mathcal{F}^t$ in our framework, both the labeled source-to-target translated images ${\mathbb{X}}^{s\to t}$ and the pseudo-labeled target-domain images ${\mathbb{X}}^{t}$ serve as informative training samples with non-overlapping real or pseudo identity labels.
In this way, we can create a unified training image set ${\mathbb{X}}={\mathbb{X}}^{s\to t} \cup {\mathbb{X}}^t$ with a unified label set to supervise a cross-domain identity classifier $\mathcal{C}^t:{\bm{f}}\rightarrow\{1,\cdots, p^s+\hat{p}^t \}$.
Target-domain data ${\mathbb{X}}^t$'s encoded features $\{{\bm{f}}^t\}$ are clustered into $\hat{p}^t$ classes and images within the same cluster are assigned the same label.
Note that the clustering-based pseudo label creation is a general pipeline in UDA tasks and is not the focus of our method.
Any common clustering algorithms can be adopted here, \textit{e}.\textit{g}.,  {$k$-means}, {DBSCAN} \cite{ester1996density}.

 The target-domain encoder ${\mathcal{F}^t}$ can then be trained in a fully-supervised manner.
Specifically,
each sample $x \in {\mathbb{X}}$ is assigned a corresponding label $y \in \{1,\cdots,p^s+\hat{p}^t\}$, and
${\mathcal{F}^t}$ is optimized with the objective function similar to source-domain encoder learning in Eq. (\ref{eq:source}),
{\small
\begin{align}
\label{eq:pic}
&\mathcal{L}_\text{enc}^t(\mathcal{F}^t,\mathcal{C}^t) =
\mathbb{E}_{x \sim {\mathbb{X}}}\left[ \ell_\text{ce} ( \mathcal{C}^t({\bm{f}}), y  )\right] +
\mathbb{E}_{x \sim {\mathbb{X}}}\left[
(\|{\bm{f}}-{\bm{f}}_p\| + m
 - \|{\bm{f}}-{\bm{f}}_n\| )^+\right].
\end{align}}%
The target-domain encoder $\mathcal{F}^t$ can therefore take full advantages of
(1) the source-to-target images translated by our $\mathcal{G}^{s\to t}$, which better maintain their inter-sample relations, and
(2) the unified target-domain label set that consists of both the valuable ground-truth source-domain identity labels and the target-domain pseudo labels.
$\mathcal{F}^t$ trained by this strategy is shown to embed more discriminative features for distinguishing target-domain identities.

In our overall framework, the source-domain encoder $\mathcal{F}^s$ is fixed after pre-training, and the structured domain-translation (SDT) network and the target-domain encoder $\mathcal{F}^t$ alternately promote each other via joint training to achieve optimal re-ID performance.
When fixing $\mathcal{F}^t$,
it measures translated inter-sample relations for regularizing SDT via
$\mathcal{L}_\text{rc}$.
When fixing SDT, it generates training samples to optimize $\mathcal{F}^t$.
Once $\mathcal{F}^t$ is further trained to achieve better re-ID performance on the target domain, it could in turn generate more accurate pseudo labels and measure more accurate data relations for further improving SDT.
After training, only $\mathcal{F}^t$ is used to encode target-domain samples into features for ranking without extra costs and parameters.
The overall algorithm is summarized in Alg. \ref{overallalg}.

\vspace{-5pt}
\section{Experiments}
\vspace{-5pt}
\subsection{Datasets}
\vspace{-5pt}
We evaluate our framework on three widely used person re-ID datasets, including DukeMTMC-reID \cite{dukemtmc}, Market-1501 \cite{market} and MSMT17 \cite{wei2018person}.
DukeMTMC-reID \cite{dukemtmc}
contains 36,411 images of 702 identities for training and another 702 identities for testing, with all the images captured from 8 cameras.
Market-1501 \cite{market} consists of 12,936 images of 751 identities for training and
19,281 images of 750 identities for testing, which are shot by 6 cameras.
MSMT17 \cite{wei2018person} is the most challenging dataset with 126,441 images of 4,101 identities from 15 cameras, where 1,041 identities are used for training.
Mean average precision (mAP) and CMC top-1/5/10 accuracies are utilized for evaluation.

\vspace{-5pt}
\subsection{Implementation Details}
\label{sec:imp}
\vspace{-5pt}

\paragraph{Network architecture.}
We adopt ResNet-50 \cite{he2016deep} as the backbone for the source-domain and target-domain person image encoders, which are initialized with ImageNet-pretrained \cite{deng2009imagenet} weights.
The target-domain encoder $\mathcal{F}^t$ and structured domain-translation network are alternately updated in each iteration to avoid unstable training.
Furthermore, we adopt a momentum encoder \cite{he2019momentum} (denoted as $\mathcal{F}^t_*$)
to replace $\mathcal{F}^t$ in Eq. (\ref{eq:rel_T}) for measuring more stable triplet relations after domain translation.
In particular,
we denote the parameters of $\mathcal{F}^t$ and $\mathcal{F}^t_*$ as $\theta^{(T)}$ and $\theta_*^{(T)}$ at iteration $T$.
$\theta_*^{(T)}$ can be calculated as $\theta_*^{(T)}=\alpha\theta_*^{(T-1)}+(1-\alpha)\theta^{(T)}$, where $\theta_*^{(0)}=\theta^{(0)}$ and $\alpha=0.999$ is the momentum coefficient.
Intuitively, the momentum encoder
could provide more reliable inter-sample relations since it
eases the training bias caused by unstable translation results.

\vspace{-5pt}
\paragraph{Training data organization.}
Each mini-batch contains 56 source-domain images of 8 ground-truth identities (7 for each identity) and 56 target-domain images of 8 pseudo identities.
The pseudo identities are assigned by clustering algorithm and updated before each epoch.
All images are resized to 256$\times$128.
Randomly erasing \cite{zhong2017random}, cropping and flipping are applied to each image.

\vspace{-5pt}
\paragraph{Network optimization.}
ADAM optimizer is adopted to optimize the networks with weighting factors $\lambda_\text{rc}=1$, $\lambda_\text{adv}=1$, $\lambda_\text{cyc}=10$, $\lambda_\text{apr}=0.5$ and the triplet margin $m=0.3$.
The initial learning rates ($lr$) are set to 0.00035 for person image encoders and 0.0002 for the structured domain-translation (SDT) network.
The source-domain pre-training iterates for 30 epochs and the learning rate decreases to 1/10 of its previous value every 10 epochs.
The proposed joint training scheme (Alg. \ref{overallalg}) iterates for 50 epochs,
where the learning rate is constant for the first 25 epochs and then gradually decreases to 0 for another 25 epochs following the formula $lr=lr\times(1.0-\max(0,epoch-25)/25)$.

\begin{table}[t]
	\tiny
	\centering
	\caption{Unsupervised domain adaptation performances by state-of-the-art methods and our proposed SDA on person re-ID datasets, \textit{e}.\textit{g}.,  DukeMTMC-reID~\cite{dukemtmc}, Market-1501~\cite{market} and MSMT17~\cite{wei2018person}.}
	\vspace{-5pt}

	\label{tab:sota}
	\begin{center}
	\begin{tabular}{P{4.1cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Methods}} & \multicolumn{4}{c|}{DukeMTMC-reID$\to$Market-1501} & \multicolumn{4}{c}{Market-1501$\to$DukeMTMC-reID} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
    PUL~\cite{fan2018unsupervised} (TOMM'18) & 20.5 & 45.5 & 60.7 & 66.7 & 16.4 & 30.0 & 43.4 & 48.5 \\
    TJ-AIDL~\cite{wang2018transferable} (CVPR'18) & 26.5 & 58.2 & 74.8 & 81.1 & 23.0 & 44.3 & 59.6 & 65.0 \\
    SPGAN~\cite{deng2018image} (CVPR'18) & 22.8 & 51.5 & 70.1 & 76.8 & 22.3 & 41.1 & 56.6 & 63.0 \\
    HHL~\cite{zhong2018generalizing} (ECCV'18) & 31.4 & 62.2 & 78.8 & 84.0 & 27.2 & 46.9 & 61.0 & 66.7 \\
    CFSM~\cite{chang2018disjoint} (AAAI'19) & 28.3 & 61.2 & - & - & 27.3 & 49.8 &- & -  \\
    BUC~\cite{lin2019aBottom} (AAAI'19) & 38.3 & 66.2 & 79.6 & 84.5 & 27.5 & 47.4 & 62.6 & 68.4 \\
    ARN~\cite{li2018adaptation} (CVPR'18-WS) & 39.4 & 70.3 & 80.4 & 86.3 & 33.4 & 60.2 & 73.9 & 79.5 \\
    UDAP~\cite{song2018unsupervised} (Arxiv'18) & 53.7 & 75.8 & 89.5 & 93.2 & 49.0 & 68.4 & 80.1 & 83.5 \\
    ECN~\cite{zhong2019invariance} (CVPR'19) & 43.0 & 75.1 & 87.6 & 91.6 & 40.4 & 63.3 & 75.8 & 80.4 \\
    UCDA~\cite{qi2019novel} (ICCV'19) & 30.9 & 60.4 & - & - & 31.0 & 47.7 & - & - \\
    PDA-Net~\cite{li2019cross} (ICCV'19) & 47.6 & 75.2 & 86.3 & 90.2 & 45.1 & 63.2 & 77.0 & 82.5 \\
    CR-GAN~\cite{chen2019instance} (ICCV'19) & 54.0 & 77.7 & 89.7 & 92.7 & 48.6 & 68.9 & 80.2 & 84.7 \\
    PCB-PAST~\cite{zhang2019self} (ICCV'19) & 54.6 & 78.4 & - & - & 54.3 & 72.4 & - & - \\
    SSG~\cite{yang2019selfsimilarity} (ICCV'19) & 58.3 & 80.0 & 90.0 & 92.4 & 53.4 & 73.0 & 80.6 & 83.2 \\
   MMT (dual ResNet-50's)~\cite{ge2020mutual} (ICLR'20) & {71.2} & {87.7} & {94.9} & {96.9} & {65.1} & {78.0} & {88.8} & {92.5} \\
    \hline \\[-2.3ex]

	Our SDA w/ {$k$-means} & {66.4} & {86.4} & {93.1} & {95.6} & {56.7} & {74.0} & {84.1} & {87.7} \\
	Our SDA w/ {DBSCAN} & \textbf{70.0} & \textbf{86.9} & \textbf{94.4} & \textbf{96.3} & \textbf{61.4} & \textbf{76.5} & \textbf{86.6} & \textbf{89.7} \\
	\hline
	Our SDA w/ {$k$-means} + MMT~\cite{ge2020mutual} & \textbf{74.3} & \textbf{89.7} & \textbf{95.9} & \textbf{97.4} & \textbf{66.7} & \textbf{79.9} & \textbf{89.1} & \textbf{92.7} \\

	\hline
	\end{tabular}\\
	 \vspace{1pt}
	\begin{tabular}{P{4.1cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Methods}} & \multicolumn{4}{c|}{Market-1501$\to$MSMT17} & \multicolumn{4}{c}{DukeMTMC-reID$\to$MSMT17} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
    PTGAN~\cite{wei2018person} (CVPR'18) & 2.9 & 10.2 & - & 24.4 & 3.3 & 11.8 & - & 27.4 \\
    ECN~\cite{zhong2019invariance} (CVPR'19) & 8.5 & 25.3 & 36.3 & 42.1 & 10.2 & 30.2 & 41.5 & 46.8 \\
    SSG~\cite{yang2019selfsimilarity} (ICCV'19) & 13.2 & 31.6 &- & 49.6 & 13.3 & 32.2 & - & 51.2 \\
    MMT (dual ResNet-50's)~\cite{ge2020mutual} (ICLR'20) & 22.9 & 49.2 & {63.1} & {68.8} & 23.3 & 50.1 & 63.9 & {69.8} \\
    \hline
    \\[-2.3ex]
    Our SDA w/ {$k$-means}  &  {20.6} & {46.8} & {59.9} & {65.0} & {23.0} & {51.7} & {64.2} & {69.6} \\
	Our SDA w/ {DBSCAN} & \textbf{23.2} & \textbf{49.5} & \textbf{62.2} & \textbf{67.7} & \textbf{25.6} & \textbf{54.4} & \textbf{66.4} & \textbf{71.3} \\
	\hline
	Our SDA w/ {$k$-means} + MMT~\cite{ge2020mutual} & \textbf{29.0} & \textbf{57.0} & \textbf{69.5} & \textbf{74.1 } & \textbf{30.3} & \textbf{59.6} & \textbf{71.7} & \textbf{76.2} \\
	\hline
	\end{tabular}
	\end{center}
\vspace{-10pt}
\end{table}

\vspace{-5pt}
\subsection{Comparison with State-of-the-arts}
\vspace{-5pt}

We compare our proposed SDA framework with state-of-the-art methods on four domain adaptive re-ID tasks in Table \ref{tab:sota}.
Our method is plug-and-play with any pseudo-label-based target domain encoder.
We tested DBSCAN \cite{ester1996density} in our encoder, namely ``Our SDA w/ {DBSCAN}". It outperforms the best-performing single models, SSG \cite{yang2019selfsimilarity} and PCB-PAST \cite{zhang2019self}, that adopted the same clustering algorithm.
We adopt the same hyper-parameters of DBSCAN as \cite{zhang2019self,yang2019selfsimilarity} for fair comparison.

We also tested $k$-means on SDA with the optimal $k$ value following the state-of-the-art \cite{ge2020mutual}, \textit{i}.\textit{e}.,
$500$ for Duke$\to$Market, $700$ for Market$\to$Duke, $1500$ for Duke$\to$MSMT and Market$\to$MSMT.
Although MMT \cite{ge2020mutual} shows superior performances over ``Ours SDA w/ $k$-means'' by adopting dual networks with two times more parameters and computations for mutual training,
our SDA is well compatible with it and can be combined to achieve further improvements,
\textit{i}.\textit{e}.,  we add the robust soft pseudo labels introduced by MMT.
The combination ``Our SDA w/ $k$-means+MMT \cite{ge2020mutual}" shows further $4.3\%$ and $5.3\%$ mAP gains on Duke$\to$Market and Market$\to$Duke.
Moreover, our SDA is consistently effective without the need of setting $k$ to be close to the actual identity numbers.
As shown in Table \ref{tab:ks}, even with different $k$'s, our SDA stably improves the already strong {baselines}, which is trained with only the target-domain samples and clustering-based pseudo labels.

Note that the focus of our SDA is to generate informative training samples rather than pseudo label refinery as the previous methods \cite{zhang2019self,yang2019selfsimilarity,ge2020mutual}. The translated images by our SDA are used as additional training samples to further improve the pseudo-label-based encoder.
As previous translation-based methods \cite{wei2018person,deng2018image,deng2018similarity,chen2019instance} did not utilize pseudo labels and therefore cannot be directly compared with, we replace our online relation-consistency regularization with their ID-based constraints (Section \ref{sec:abla}) to show the advantages of our well-designed regularization.

\begin{figure}[tb]
\begin{floatrow}[2]
\killfloatstyle
 \tablebox{\caption{Comparison with different values of $k$ in our SDA when adopting $k$-means on Market$\to$Duke.}\label{fig:rp2}}{%
 \vspace{-10pt}
  \tiny
	\centering
	\label{tab:ks}
	\begin{center}
	\begin{tabular}{P{0.6cm}|C{0.8cm}C{0.8cm}|C{1cm}C{1cm}}
	\hline

	\multicolumn{1}{c|}{\multirow{2}{*}{$k$ value}} & \multicolumn{2}{c|}{Baseline} & \multicolumn{2}{c}{Ours} \\
	\cline{2-5}
	\multicolumn{1}{c|}{} & mAP & top-1 & mAP & top-1 \\
	\hline \hline
    \\[-2.3ex]
   \centering 500 &  46.7 & 65.9 & 53.8 \textbf{(+7.1)}  & 70.6 \textbf{(+4.7)}  \\
   \centering 700 &   50.1 & 68.2  & 56.7 \textbf{(+6.6)} & 74.0 \textbf{(+5.8)} \\
  \centering  900 &   48.9 & 66.6  & 56.0 \textbf{(+7.1)} & 72.9 \textbf{(+6.3)}\\
	\hline
	\end{tabular}
	\end{center}}%
\tablebox{\caption{Comparison with the optional relation-consistency regularizations in our SDA with $k$-means.}\label{fig:rp1}}{%
\vspace{-10pt}
\tiny
	\centering
	\label{tab:options}
	\begin{center}
	\begin{tabular}{P{2.4cm}|C{0.5cm}C{0.6cm}|C{0.5cm}C{0.6cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Regularization}} & \multicolumn{2}{c|}{Duke$\to$Market} & \multicolumn{2}{c}{Market$\to$Duke} \\
	\cline{2-5}
	\multicolumn{1}{c|}{} & mAP & top-1 & mAP & top-1 \\
	\hline \hline
    \\[-2.3ex]
    Prediction-consistency $\mathcal{L}_\text{pc}$ & 63.3 & 84.5 & 54.3 & 71.1 \\
    Batch-all relations $\mathcal{L}_\text{brc}$ & 64.6 & 86.0 & 54.6 & 71.5 \\

    \hline
	Our $\mathcal{L}_\text{rc}$ & \textbf{66.4} & \textbf{86.4}  & \textbf{56.7} & \textbf{74.0} \\
	\hline
	\end{tabular}
	\end{center}}%
\end{floatrow}
\vspace{-5pt}
\end{figure}

\vspace{-5pt}
\subsection{Ablation Studies}
\label{sec:abla}
\vspace{-5pt}

\begin{table*}[t]
\tiny
	\centering
	\caption{Ablation studies for our proposed framework (w/ $k$-means) on individual components. }
\vspace{-5pt}
	\label{tab:ablation}
	\begin{center}
	\begin{tabular}{P{4.5cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Methods}} & \multicolumn{4}{c|}{DukeMTMC-reID$\to$Market-1501} & \multicolumn{4}{c}{Market-1501$\to$DukeMTMC-reID} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
    Source-domain pre-trained & 21.7 & 47.8 & 64.2 & 70.1 & 14.4 & 26.7 & 39.8 & 45.6 \\
    Baseline (only target-domain data + pseudo labels) & 59.0 & 80.7 & 90.5 & 93.4 & 50.1 & 68.2 & 79.2 & 82.7 \\
    Baseline + raw source-domain data & 53.2 & 77.5 & 88.9 & 92.1 & 50.8 & 69.1 & 80.1 & 83.1 \\
    \hline

    Ours w/o relation-consistency loss $\mathcal{L}_\text{rc}$ & 63.0 & 84.3 & 92.7 & 95.2 & 52.9 & 69.8 & 81.3 & 84.5 \\
    Ours w/ $\mathcal{L}_\text{rc} \to$ contrastive loss regul. \cite{deng2018image} & 56.5 & 78.7 & 91.5 & 94.0 & 51.7 & 69.9 & 80.5 & 83.3 \\
    Ours w/ $\mathcal{L}_\text{rc} \to$ classification loss regul.  \cite{deng2018similarity,chen2019instance} & 63.4 & 84.9 & 92.7 & 95.1 & 53.8 & 70.9 & 81.9 & 85.6 \\
    Ours w/ $\mathcal{L}_\text{rc} \to$ triplet loss regul. & 64.1 & 85.2 & 92.9 & 95.4 & 54.5 & 72.1 & 82.3 & 85.9 \\
    \hline
    Ours w/o unified label system & 64.8 & 86.0 & 93.1 & 95.2 & 54.8 & 73.1 & 83.1 & 85.5 \\

    Ours w/o momentum encoder \cite{he2019momentum} & 65.3 & 86.1 & 93.1 & 95.3 & 55.3 & 72.5 & 82.8 & 85.5 \\
    \hline
    \\[-2.3ex]

	Ours (full) & \textbf{66.4} & \textbf{86.4} & \textbf{93.1} & \textbf{95.6} & \textbf{56.7} & \textbf{74.0} & \textbf{84.1} & \textbf{87.7} \\
	\hline
	\end{tabular}
	\end{center}
	\vspace{-10pt}
\end{table*}

We conduct ablation studies on Duke$\to$Market and Market$\to$Duke tasks to analyze the effectiveness of our framework and the importance of the proposed online relation-consistency regularization term.
Detailed ablation experiments can be found in Table \ref{tab:ablation}.

\vspace{-5pt}
\paragraph{Compatibility to different target-domain encoders.}

Our proposed SDA with online relation regularization is general and can benefit different target-domain encoders.
We treat the target-domain encoder $\mathcal{F}^t$ trained with only target-domain images and clustering-based pseudo labels as our \emph{baseline} model.
Our framework significantly outperforms the baseline model by properly exploiting valuable source-domain data (see ``Ours (full)'' vs. ``Baseline'' in Table \ref{tab:ablation}).
A na\"ive way to use source-domain images is to directly train on both domains' raw images, denoted as  ``Baseline+raw source-domain data''.
The performance is even worse than the baseline on Duke$\to$Market due to the large domain gaps, which indicates the necessity of properly leveraging different domains' images.

Our SDA can also be integrated and benefit state-of-the-art pseudo-label-based method \cite{ge2020mutual} (see ``Our SDA+MMT'' vs. ``MMT'' in Table \ref{tab:sota}).
The improvements demonstrate the effectiveness of our structured domain translation
on either baseline or state-of-the-art target-domain encoders.

\vspace{-5pt}
\paragraph{Comparison with existing ID-based regularizations on translation.}

Existing translation-based UDA methods \cite{deng2018image,wei2018person,deng2018similarity,chen2019instance} adopted identity-based losses with static targets to regularize the domain translation, including contrastive loss in SPGAN \cite{deng2018image}, classification loss in eSPGAN \cite{deng2018similarity} and CR-GAN \cite{chen2019instance}, and triplet loss. Generally, the previous losses only require the source-to-target translated images to be correctly classified after translation.
Since our pseudo-label-based target-domain encoder shows much better baseline performance than theirs,
for fair comparison,
we replace the online relation regularization $\mathcal{L}_\text{rc}$ in our framework with the previous methods' regularizations to demonstrate the effectiveness of our regularization.

The results are reported in Table \ref{tab:ablation}.
We observe that replacing our proposed $\mathcal{L}_\text{rc}$ with previous regularization (denoted as ``Ours w/ $\mathcal{L}_\text{rc} \to$ contrastive loss regul. \cite{deng2018image}'', ``Ours w/ $\mathcal{L}_\text{rc} \to$ classification loss regul.  \cite{deng2018similarity,chen2019instance}'',
``Ours w/ $\mathcal{L}_\text{rc} \to$ triplet loss regul.'') all lead to worse performances than our method, demonstrating the superiority of our stronger relation regularization term over the weaker regularizations in previous translation-based UDA methods.

To show the necessity of adopting relation regularization during translation, we also tested totally removing $\mathcal{L}_\text{rc}$ from our framework, dubbed ``Ours w/o $\mathcal{L}_\text{rc}$'' in Table \ref{tab:ablation}.
Significant mAP decreases of $3.4\%$ and $3.8\%$ are observed on Duke$\to$Market and Market$\to$Duke tasks.

\vspace{-5pt}
\paragraph{Alternative designs of online relation-consistency regularization.}

Our SDT applies regularizations on the softmax-triplet relations (Eq. (\ref{eq:sc})).
We further explore two alternative forms, prediction-consistency regularization $\mathcal{L}_\text{pc}$ and batch-all relation-consistency regularization $\mathcal{L}_\text{brc}$ (Table \ref{tab:options})
to verify the effectiveness of our well-designed inter-sample relation constraint.

Specifically, the prediction-consistency regularization ensures that each individual image in the source domain should maintain the same ``soft'' class prediction after source-to-target translation.
The loss function is formulated as $\mathcal{L}_\text{pc}(\mathcal{G}^{s\to t})=
\mathbb{E}_{x^s\sim {\mathbb{X}}^s} [ - \mathcal{C}^s({\bm{f}}^s) \cdot \log (\mathcal{C}^t({\bm{f}}^{s \to t})) ]$.
As shown in Table \ref{tab:options},
$3.1\%$ and $2.4\%$ mAP drops are observed on two datasets.

Our $\mathcal{L}_\text{rc}$ (Eq. (\ref{eq:sc})) aims at preserving relations within hardest triplets,
while the alternative batch-all relation-consistency loss  $\mathcal{L}_\text{brc}$ tries to preserve all such relations within batches.
We model the batch-all inter-sample relations by measuring
the similarities $\mathcal{R}(x^s;\mathcal{F}^s)=[\langle {\bm{f}}^s, {\bm{f}}_1^s \rangle,\cdots,\langle {\bm{f}}^s, {\bm{f}}_k^s \rangle]$,
which consist of pairwise dot products between each sample $x^s$ and all other ones in the same batch.
The similarity vector is normalized and a soft cross-entropy loss is adopted to regularize all the relations after translation,
$\mathcal{L}_\text{brc}(\mathcal{G}^{s\to t})=\mathbb{E}_{x^s\sim {\mathbb{X}}^s} [ - \mathcal{R}(x^s; \mathcal{F}^s) \cdot \log \mathcal{R}(x^s; \mathcal{G}^{s \to t}, \mathcal{F}^t) ]$.
1.8\% mAP and 2.1\% mAP drops can be observed on the two tasks.
The reason might be that
batch-all relations contain many easy cases that cannot provide effective supervisions for training.

\vspace{-5pt}
\paragraph{Effectiveness of training with the unified label set.}
We observe that the target-domain encoder also benefits from the unified label set by training the classifier on all the $p^s+\hat{p}^t$ classes across the two domains.
To show it, we design an experiment with separate classifiers for source-to-target translated images and target-domain images,
\textit{i}.\textit{e}.,  ${{\cal C}^t}:{\bm{f}}\rightarrow\{1,\cdots, p^s+\hat{p}^t \}$ is split into $\mathcal{C}^{s\to t}:{\bm{f}}^{s\to t} \rightarrow\{1,\cdots, p^s\}$ and $\mathcal{C}^t:{\bm{f}}^t \rightarrow\{1,\cdots, \hat{p}^t \}$.
We report the performance in Table \ref{tab:ablation} as ``Ours w/o unified label system''.
1.6\% and 1.9\% mAP drops are observed on the two tasks, which indicate the effectiveness of modeling the relations between two domains.

\vspace{-5pt}
\paragraph{Further benefits from the momentum encoder $\mathcal{F}^t_*$.}
As described in Section \ref{sec:imp}, we utilize a momentum encoder \cite{he2019momentum} for more stable training and better performance.
To verify that the main contribution is not from the momentum encoder,
we perform an experiment by removing  $\mathcal{F}^t_*$ while keeping all other components unchanged.
The experimental results are denoted as ``Ours w/o momentum encoder \cite{he2019momentum}'' in Table \ref{tab:ablation}.
We observe slight drops of 1.1\% and 1.4\% mAP on two tasks.

\vspace{-5pt}
\section{Discussion and Conclusion}
\vspace{-5pt}
In this work, we propose an end-to-end structured domain adaptation framework with a novel online relation-consistency regularization term to tackle the unsupervised domain adaptation (UDA) problem for person re-ID. The structuredly translated images in our method are shown to be informative samples for improving the training of pseudo-label-based encoder. The joint optimization scheme of domain-translation network and re-ID encoder is effective, however, it still has difficulty on handling industrial-scale datasets. Further improvements are called for. Beyond the person re-ID, our proposed inter-sample relation-consistency regularization may benefit other related UDA tasks.

\section*{Broader Impact}

The goal of the present work is to tackle the challenge of properly transferring the learned knowledge to a new domain without any manual annotations.
The applications of our proposed person re-ID algorithm will greatly contribute to economic and social development. In particular, our method can be applied in the construction of smart cities with annotation-free models covering retail, transportation, as well as security.
In addition, our proposed framework is by no means limited to the task of person re-ID. It will deepen our understanding of unsupervised and semi-supervised representation learning when extended to broader research topics.

The applications of re-ID systems, however, inevitably run the risk of privacy infringement, which calls for careful regulations from relevant authorities. Another issue that may cause concern is the possible results of misidentification, which is why we should always be cautious when adopting the results of re-ID technologies.

\small
\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


Kakadu – Hostels in Kakadu. Maps for Kakadu, Photos and Reviews for each hostel in Kakadu.
Looking for a hostel in Kakadu? Look no further, all the good deals in Kakadu are on Dorms.com. Like thousands of other people every month, book your hostel securely at the best price in all neighbourhoods of Kakadu. On this site, you can easily find a hostel downtown, in a trendy neighbourhood; a hostel in the student- university district, close to bus or train stations, airports or major attractions of Kakadu.


In 2013, ten individuals from Pierce County collectively filed suit contesting their petitions due to the waits they experienced for appropriate evaluation and care. Last August, the Washington State Supreme Court ruled on the case, finding that the practice of ED "psychiatric boarding" until an appropriate bed is available is unconstitutional and therefore unlawful (see Forbes article). Hospitals and the Department of Social and Health Services (DSHS) were given until December 26, 2014 to comply.
The investment was immediate. The Puget Sound Business Journal described the state's rapid response in an article, "Washington care providers race to expand services." The day following the Supreme Court ruling, the Regional Service Network for Pierce County, Optum Health, approached Recovery Innovations to fast-track the opening of a new Evaluation and Treatment (E&T) program on the campus of Western State Hospital, co-located with a similar program operated by Telecare Corporation, to quickly expand important capacity for those individuals in need.
Optum Health has been a nationally recognized leader for its innovative recovery and crisis service system of care in Pierce County, led by CEO Cheri Dolezal. Through a strong focus on community collaboration, measured accountability, and enhanced service and peer capacity, Optum Health has increased the overall number of people served by 32% (see PowerPoint presentation). So, it's no surprise that Cheri's team acted quickly to take advantage of an opportunity to further improve the system with additional E&T capacity.
On December 8, 2014, RI opened the Lakewood Recovery Pathway. It's a 16-bed inpatient Evaluation and Treatment program, serving individuals with mental illness under 72-hour involuntary detention and/or 14-day commitment in accordance with the Washington State Involuntary Treatment Act, RCW 71.05. It's a formal, legal process, which could be cold and distant. But, I'm very proud of the progress being made by the RI team in partnership with Optum Health to bring the strongest possible recovery experience and healing space for individuals in crisis.
Cheri has been an inspiration throughout the implementation. She wrote to me early on about starting an E&T in Clark County when she was Director for the Regional Service Network and working to incorporate a welcome and positive recovery environment. Individuals most frequently come into involuntary care after interaction with family, community, and/or law enforcement has resulted in an assessment and referral to an E&T by a Designated Mental Health Professional (DMHP). The individual may be very frightened by what they are experiencing and/or the process itself. Compassion, respect, and human dignity make all the difference in whether the experience is an important step towards recovery or a traumatic experience that ultimately doesn't help, but does harm.
In her email, Cheri described creating a comfort room in the E&T for the "guests," complete with a recliner and very soft, cuddly, warm blankets. Calm music played in the background, and the psychiatrist ate lunch with the group every day, integrating staff and those on petition. The strong nursing leadership, who directed the program, helped weave the recovery, clinical, and medical together.
This hands-on support by Cheri and the entire Optum Health team has been terrific. I'm also extremely grateful to RI senior leaders from Phoenix who have spent weeks on the ground supporting the new team in Lakewood. These include Leon Boyko, Dr. Vernon Barksdale, Marie Gagnon, Kathleen Miner, and Andrew Terech. Dr. Joshua Shemon has also been phenomenal as the administrator for the E&T and the RI Recovery Response Center (Crisis Stabilization Program) in Fife. His calm and steady leadership has been vital in supporting new staff and reinforcing a recovery space.
I also wish to thank Eleanor Owen, a member of RI's Board of Directors and a cornerstone in behavioral health, family advocacy, and the recovery movement for nearly 40 years, for touring the new space, meeting with community leaders and supporting the staff during this launch.
On Friday, I visited the Lakewood facility to recognize the entire team for going above and beyond, and we recognized Liz Timko with the Velvet Elvis* award. Liz has been a senior leader at the RI flagship facility in Peoria, Arizona, and we are so proud and appreciative of her efforts in Washington State. She has spent nearly two months providing hands-on support to new staff, showing them firsthand how to interact and create recovery partnerships. Her expertise as a Certified Therapeutic Options and CPI trainer, her ability to handle and manage crisis situations, and her knowledge of RI's internal systems have been invaluable.
I'm also excited to announce two important new hires. Beth Hammonds joins the RI senior team as the new Regional Director for the Washington State and California programs, starting today. Beth helped launch the Lakewood E&T and brings a rich experience from her years as Adult Outpatient Director for King County-based Valley Cities. In addition to the Fife RRC and Lakewood E&T, Recovery Innovations also operates the Peer Bridger and Community Building program. Beth will oversee these programs as well as the operations in California, and be responsible for the Customer relationships with RI's funders.
On March 23rd, Rivers Carpenter will start as the administrator for the Lakewood E&T and will report to Joshua, alongside Gretchen McClelland who leads the Recovery Response Center in Fife. She is an LCSW with a strong background in managing clinical and residential programs.
Beth Hammonds joins Recovery Innovations as Regional Director for California and Washington State. Rivers Carpenter will be the Lakewood E&T Administrator.
Velvet Elvis Update: Liz is the second individual at Recovery Innovations to join the ranks of Velvet Elvis recipient. After a 1,200 mile journey, the king transitions to her from Sarah Blanka who held the award in Peoria, Arizona for 30 days.
In behavioral health, recovery education opens an individual's eyes to recognize potential beyond the despair of a limited life; giving courage and creating possibilities. Perhaps this is seen most strongly in those who have transitioned from a disability check to meaningful employment.
But recovery education creates more than personal development; it's the stuff of social movements. While token voice and limited participation may previously have been seen as laudable for a person wanting to be involved in a community mental health center or mental health system, suddenly active leadership and action are required.
Three departments within RI are responsible for delivering this education.
Founded in 2005 as an RI subsidiary, the Recovery Opportunity Center has graduated more than 6,000 individuals from Peer Employment Training, certifying them to serve in Peer Support roles. As a result, RI has boasted one of the largest peer supports workforces in the world, and has used this experience in recruitment, training, support, management and retention of peer workers to create trainings that support other organizations in making similar transformations.
Lisa St. George leads the Recovery Opportunity Center and its group of six master recovery trainers who have delivered recovery consultation to behavioral health leaders around the world. In addition to trainings that guide people who supervise peer workers about how to develop a resilient, effective, and viable peer workforce, the ROC has also developed numerous training tools, books, and workbooks that support the recovery of the people RI serves.
Author Malcolm Gladwell's Ted Talk on Capitalizing Human Potential focuses on two key drivers: what others expect of us and what we are trained to achieve. Often, individuals with serious mental illness are told that they have a disabling illness, and as a result they will not be able to work.
Since 2002, the Company has offered alternatives to historic day programs, including vocational certificates and personal growth workshops, bringing an important "Peer Career Ladder" to thousands of individuals served by RI's Wellness City. This empowers individuals to choose career options beyond Certified Peer Supports.
Arrow Foster leads the Recovery Education Center, which is licensed as a private post-secondary vocational school in Arizona. The program equips individuals for roles as wellness coaches, crisis navigators, case managers, and supervisors and/or administrators. The master's level instructors also introduce students to work roles outside of behavioral health.
Chris Martin leads the Learning Team of three master learning specialists and an education coordinator, which supports RI sites and programs across the US and in Auckland, New Zealand. They also focus on the professional and career development of staff and the advancement of the four balanced score card areas of the company.
Those who join RI's nearly 800 strong workforce onboard through "New Employee Celebration" week, and receive ongoing support and skill-building through strong, interactive face-to-face and virtual trainings. This preparation is vital for all employees to help maintain the exceptionally strong connection to the RI core mission and to a service approach that is "irresistible" to those who experience it.
In 2015, RI is aligning these three important departments under a new role to integrate and elevate their outcomes. Effective March 9, Shanna Galdys will join the RI executive team as Chief Recovery and Clinical Officer. Her team will include the learning department leaders Lisa St. George (ROC), Arrow Foster (REC) and Chris Martin (Learning Team).
Shanna previously led the learning efforts at two prior Maricopa County Regional Behavioral Health Authorities (Magellan Health and Value Options), and is a recognized leader in behavioral health in Maricopa County with a demonstrated and enduring commitment to positively impact the lives of individuals with serious mental illness.
Her prior successes include an outcomes-based workforce development model that continuously engaged participants in the practical application of recovery principles, utilized a blended learning model, and incorporated licensing and credentialing requirements. The RBHA facilitated tens of thousands of "learning events" per year, supporting new employees, behavioral health provider staff, and community members with both face-to-face and online training.
Shanna's credentials align strongly with RI's mission and values. She is a family member of an individual with a serious mental illness, an active member of NAMI, has a Masters of Social Work (MSW) degree and is also a Certified Psychiatric Rehabilitation Practitioner (CPRP).
In addition to working closely with the three learning departments, Shanna will partner with the RI operations leaders to strengthen the clinical foundation of the Company's recovery programs. This includes creating strategies to improve the central focus on the individual, the health and safety of staff and guests/citizens, and the measurement of effectiveness. Shanna will also be interviewing and surveying RI staff about what they need to better serve individuals, including those with co-occurring addiction use, serious medical complications, and/or suicide risk.
Shanna Galdys joins Recovery Innovations as Chief Recovery and Clinical Officer.