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Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Domain adaptation for object recognition: An unsupervised approach
Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Domain generalization for object recognition with multi-task autoencoders
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 2016
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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Multi-task self-supervised visual learning
Carl Doersch and Andrew Zisserman · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
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Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2017
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb · 2017
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Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Self-supervised adaptation of high-fidelity face models for monocular performance tracking
Jae Shin Yoon, Takaaki Shiratori, Shoou-I Yu, and Hyun Soo Park · 2019
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Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong · 2019
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Self-supervised learning for domain adaptation on point-clouds
Idan Achituve, Haggai Maron, and Gal Chechik · 2020
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Self-labelling via simultaneous clustering and representation learning
Yuki M. Asano, Christian Rupprecht, and Andrea Vedaldi · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Action segmentation with joint self-supervised temporal domain adaptation
Min-Hung Chen, Baopu Li, Yingze Bao, Ghassan AlRegib, and Zsolt Kira · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Metric-guided prototype learning
Vivien Sainte Fare Garnot and Loic Landrieu · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Self-supervised domain adaptation for patient-specific, real-time tissue tracking
Sontje Ihler, Felix Kuhnke, Max-Heinrich Laves, and Tobias Ortmaier · 2020
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Self-supervised sim-to-real adaptation for visual robotic manipulation
Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe, Konstantinos Bousmalis, and Francesco Nori · 2020
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Implicit class-conditioned domain alignment for unsupervised domain adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin, and Mohammad Havaei · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2020
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Cross-domain self-supervised learning for domain adaptation with few source labels
Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh, Bryan A Plummer, Stan Sclaroff, and Kate Saenko · 2020
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Pre-training by completing point clouds
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matthew J Kusner · 2020
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Unsupervised domain adaptation via structured prediction based selective pseudo-labeling
Qian Wang and Toby Breckon · 2020
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