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We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence.
Analysis of representations for domain adaptation
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Sinno Jialin Pan and Qiang Yang · 2009
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Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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XuanLong Nguyen, Martin J Wainwright, Michael I Jordan, et al · 2009
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A theory of learning from different domains
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Impossibility theorems for domain adaptation
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Corinna Cortes, Yishay Mansour, and Mehryar Mohri · 2010
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Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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A pac-bayesian approach for domain adaptation with specialization to linear classifiers
Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant · 2013
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Transfer feature learning with joint distribution adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2013
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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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Domain adaptation–can quantity compensate for quality?
Shai Ben-David and Ruth Urner · 2014
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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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General overview of imageclef at the clef 2015 labs
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
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Upper bounds on the relative entropy and rényi divergence as a function of total variation distance for finite alphabets
Igal Sason and Sergio Verdú · 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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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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A new pac-bayesian perspective on domain adaptation
Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant · 2016
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Domain adaptation with conditional transferable components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 2016
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Adversarial discriminative domain adaptation
Universal domain adaptation
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2019
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On the value of target data in transfer learning
Steve Hanneke and Samory Kpotufe · 2019
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Lecture notes on information theory, May 2019
Yury Polyanskiy and Yihong Wu · 2019
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Domain adaptation with asymmetrically-relaxed distribution alignment
Yifan Wu, Ezra Winston, Divyansh Kaushik, and Zachary Lipton · 2019
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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Adaptation based on generalized discrepancy
Corinna Cortes, Mehryar Mohri, and Andrés Munoz Medina · 2019
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Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Open set domain adaptation
Pau Panareda Busto and Juergen Gall · 2017
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Theoretical analysis of domain adaptation with optimal transport
Ievgen Redko, Amaury Habrard, and Marc Sebban · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Partial adversarial domain adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long, and Jianmin Wang · 2018
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
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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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Support and invertibility in domain-invariant representations
Fredrik Johansson, David Sontag, and Rajesh Ranganath · 2019
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On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Generalized domain adaptation with covariate and label shift co-alignment
Shuhan Tan, Xingchao Peng, and Kate Saenko · 2019
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Optimal transport for multi-source domain adaptation under target shift
Ievgen Redko, Nicolas Courty, Rémi Flamary, and Devis Tuia · 2019
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Progressive feature alignment for unsupervised domain adaptation
Chaoqi Chen, Weiping Xie, Wenbing Huang, Yu Rong, Xinghao Ding, Yue Huang, Tingyang Xu, and Junzhou Huang · 2019
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Learning disentangled semantic representation for domain adaptation
Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, and Zhifeng Hao · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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Multi-source domain adaptation for text classification via distancenet-bandits
Han Guo, Ramakanth Pasunuru, and Mohit Bansal · 2020
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A survey on domain adaptation theory
Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, and Younès Bennani · 2020
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Representation bayesian risk decompositions and multi-source domain adaptation
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