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We propose a simple but effective source-free domain adaptation (SFDA) method.
Neighbourhood components analysis
Jacob Goldberger, Geoffrey E Hinton, Sam Roweis, and Russ R Salakhutdinov · 2004
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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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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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Standardized mutual information for clustering comparisons: one step further in adjustment for chance
Simone Romano, James Bailey, Vinh Nguyen, and Karin Verspoor · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 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 residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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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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Deep adaptive image clustering
Jianlong Chang, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan · 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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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, 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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Transferable representation learning with deep adaptation networks
Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 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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A dirt-t approach to unsupervised domain adaptation
Rui Shu, Hung H Bui, Hirokazu Narui, and Stefano Ermon · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang · 2019
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Unsupervised domain adaptation via regularized conditional alignment
Safa Cicek and Stefano Soatto · 2019
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Cluster alignment with a teacher for unsupervised domain adaptation
Zhijie Deng, Yucen Luo, and Jun Zhu · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Unsupervised domain adaptation via structurally regularized deep clustering
Hui Tang, Ke Chen, and Kui Jia · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Dual mixup regularized learning for adversarial domain adaptation
Yuan Wu, Diana Inkpen, and Ahmed El-Roby · 2020
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Reliable weighted optimal transport for unsupervised domain adaptation
Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and Jindong Wang · 2020
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Unsupervised domain adaptation without source data by casting a bait
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2020
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Xu Ji, Joao F Henriques, and Andrea Vedaldi · 2019
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Sliced wasserstein discrepancy for unsupervised domain adaptation
Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 2019
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Transferable attention for domain adaptation
Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long, and Jianmin Wang · 2019
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Deep comprehensive correlation mining for image clustering
Jianlong Wu, Keyu Long, Fei Wang, Chen Qian, Cheng Li, Zhouchen Lin, and Hongbin Zha · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Ruijia Xu, Guanbin Li, Jihan Yang, and Liang Lin · 2019
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Domain-symmetric networks for adversarial domain adaptation
Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan · 2019
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Bridging theory and algorithm for domain adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan · 2019
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Label propagation with augmented anchors: A simple semi-supervised learning baseline for unsupervised domain adaptation
Yabin Zhang, Bin Deng, Kui Jia, and Lei Zhang · 2020
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Fast batch nuclear-norm maximization and minimization for robust domain adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian · 2021
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With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2021
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Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu · 2021
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Exploring non-contrastive representation learning for deep clustering
Zhizhong Huang, Jie Chen, Junping Zhang, and Hongming Shan · 2021
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi · 2021
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Contrastive clustering
Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, and Xi Peng · 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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Cycle self-training for domain adaptation
Hong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density
Kuniaki Saito, Donghyun Kim, Piotr Teterwak, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2021
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You never cluster alone
Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip HS Torr, and Ling Shao · 2021
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Mice: Mixture of contrastive experts for unsupervised image clustering
Tsung Wei Tsai, Chongxuan Li, and Jun Zhu · 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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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Joost van de Weijer, Luis Herranz, Shangling Jui, et al · 2021
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Generalized source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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Distill and fine-tune: Effective adaptation from a black-box source model
Jian Liang, Dapeng Hu, Ran He, and Jiashi Feng · 2022
Closest in time.
Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
Closest in time.