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To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset (target).
Transductive inference for text classification using support vector machines
Thorsten Joachims et al · 1999
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, and Alex Smola · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, and Motoaki Kawanabe · 2007
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Domain transfer svm for video concept detection
Lixin Duan, Ivor W Tsang, Dong Xu, and Stephen J Maybank · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Discriminative clustering by regularized information maximization
Ryan Gomes, Andreas Krause, and Pietro Perona · 2010
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2010
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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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Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
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Unsupervised visual domain adaptation using subspace alignment
Basura Fernando, Amaury Habrard, Marc Sebban, and Tinne Tuytelaars · 2013
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Stability and hypothesis transfer learning
Ilja Kuzborskij and Francesco Orabona · 2013
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Generative adversarial nets
Ian J 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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Domain adaptation in the absence of source domain data
Boris Chidlovskii, Stéphane Clinchant, and Gabriela Csurka · 2016
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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 residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P Kingma · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Domain adaptation for visual applications: A comprehensive survey
Gabriela Csurka · 2017
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Domain adaptation by mixture of alignments of second-or higher-order scatter tensors
Piotr Koniusz, Yusuf Tas, and Fatih Porikli · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 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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Autodial: Automatic domain alignment layers
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, and Samuel Rota Bulo · 2017
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Yang Zhang, Philip David, and Boqing Gong · 2017
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Partial adversarial domain adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long, and Jianmin Wang · 2018
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Domain adaptive faster r-cnn for object detection in the wild
Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Minimum class confusion for versatile domain adaptation
Ying Jin, Ximei Wang, Mingsheng Long, and Jianmin Wang · 2020
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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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A balanced and uncertainty-aware approach for partial domain adaptation
Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, and Jiashi Feng · 2020
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Stochastic classifiers for unsupervised domain adaptation
Zhihe Lu, Yongxin Yang, Xiatian Zhu, Cong Liu, Yi-Zhe Song, and Tao Xiang · 2020
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
Cited alongside, same era.
Aggregating randomized clustering-promoting invariant projections for domain adaptation
Jian Liang, Ran He, Zhenan Sun, and Tieniu Tan · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Universal domain adaptation through self supervision
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko · 2020
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Unsupervised domain adaptation via structurally regularized deep clustering
Hui Tang, Ke Chen, and Kui Jia · 2020
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Your classifier can secretly suffice multi-source domain adaptation
Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, and R Venkatesh Babu · 2020
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Curriculum manager for source selection in multi-source domain adaptation
Luyu Yang, Yogesh Balaji, Ser-Nam Lim, and Abhinav Shrivastava · 2020
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Revisiting knowledge distillation via label smoothing regularization
Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, and Jiashi Feng · 2020
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Unsupervised multi-source domain adaptation without access to source data
Sk Miraj Ahmed, Dripta S Raychaudhuri, Sujoy Paul, Samet Oymak, and Amit K Roy-Chowdhury · 2021
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Confident anchor-induced multi-source free domain adaptation
Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, and Tongliang Liu · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Unbalanced minibatch optimal transport; applications to domain adaptation
Kilian Fatras, Thibault Séjourné, Rémi Flamary, and Nicolas Courty · 2021
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Kd3a: Unsupervised multi-source decentralized domain adaptation via knowledge distillation
Hao-Zhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, and Wei Chen · 2021
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Adversarial domain adaptation with prototype-based normalized output conditioner
Dapeng Hu, Jian Liang, Qibin Hou, Hanshu Yan, and Yunpeng Chen · 2021
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Re-energizing domain discriminator with sample relabeling for adversarial domain adaptation
Xin Jin, Cuiling Lan, Wenjun Zeng, and Zhibo Chen · 2021
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Self-knowledge distillation with progressive refinement of targets
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon, and Sangheum Hwang · 2021
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Semantic concentration for domain adaptation
Shuang Li, Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, and Wei Li · 2021
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Dynamic transfer for multi-source domain adaptation
Yunsheng Li, Lu Yuan, Yinpeng Chen, Pei Wang, and Nuno Vasconcelos · 2021
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Domain adaptation with auxiliary target domain-oriented classifier
Jian Liang, Dapeng Hu, and Jiashi Feng · 2021
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Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
Jian Liang, Dapeng Hu, Yunbo Wang, Ran He, and Jiashi Feng · 2021
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Information-theoretic regularization for multi-source domain adaptation
Geon Yeong Park and Sang Wan Lee · 2021
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A prototype-oriented framework for unsupervised domain adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, and Mingyuan Zhou · 2021
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Adaptive adversarial network for source-free domain adaptation
Haifeng Xia, Handong Zhao, and Zhengming Ding · 2021
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Transformer-based source-free domain adaptation
Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, and Elisa Ricci · 2021
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Unsupervised domain adaptation of black-box source models
Haojian Zhang, Yabin Zhang, Kui Jia, and Lei Zhang · 2021
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