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Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros · 2011
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 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 domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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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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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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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Central moment discrepancy (CMD) for domain-invariant representation learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz · 2017
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Domain invariant and class discriminative feature learning for visual domain adaptation
Shuang Li, Shiji Song, Gao Huang, Zhengming Ding, and Cheng Wu · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan · 2018
Cited alongside, same era.
Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Learning disentangled semantic representation for domain adaptation
Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, and Zhifeng Hao · 2019
Cited alongside, same era.
Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang · 2019
Cited alongside, same era.
Convolutional networks with dense connectivity
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens Van Der Maaten, and Kilian Weinberger · 2019
Cited alongside, same era.
Sliced wasserstein discrepancy for unsupervised domain adaptation
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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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh · 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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Transfer learning with dynamic distribution adaptation
Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, and Qiang Yang · 2020
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Unsupervised domain adaptation via structured prediction based selective pseudo-labeling
Qian Wang and Toby P. Breckon · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 2019
Cited alongside, same era.
Joint adversarial domain adaptation
Shuang Li, Chi Harold Liu, Binhui Xie, Limin Su, Zhengming Ding, and Gao Huang · 2019
Cited alongside, same era.
Transferrable prototypical networks for unsupervised domain adaptation
Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, and Tao Mei · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller · 2019
Cited alongside, same era.
BERT is not a knowledge base (yet): Factual knowledge vs. name-based reasoning in unsupervised QA
Nina Pörner, Ulli Waltinger, and Hinrich Schütze · 2019
Cited alongside, same era.
Domain-symmetric networks for adversarial domain adaptation
Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan · 2019
Cited alongside, same era.
Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian · 2020
Cited alongside, same era.
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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Cross-domain gradient discrepancy minimization for unsupervised domain adaptation
Zhekai Du, Jingjing Li, Hongzu Su, Lei Zhu, and Ke Lu · 2021
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Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification
Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Juanzi Li, and Maosong Sun · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Domain adaptation with invariant representation learning: What transformations to learn?
Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime Carbonell, and Kun Zhang · 2021
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Unsupervised domain adaptation via structurally regularized deep clustering
Ni Xiao and Lei Zhang · 2021
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Factual probing is [MASK]: learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen · 2021
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2021
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