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Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc.
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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Training generative neural networks via maximum mean discrepancy optimization
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Deep learning face attributes in the wild
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Learning transferable features with deep adaptation networks
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Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation
Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, and Wangmeng Zuo · 2017
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Re-weighted adversarial adaptation network for unsupervised domain adaptation
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MGAN: training generative adversarial nets with multiple generators
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Arnab Ghosh, Viveka Kulharia, Vinay P Namboodiri, Philip HS Torr, and Puneet K Dokania · 2017
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
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Mixture of gans for clustering
Yang Yu and Wen-Ji Zhou · 2018
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