{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adversarial-discriminative-domain-adaptation","title":"Adversarial Discriminative Domain Adaptation","arxiv_id":"1702.05464","date":"2017-02-17","proceeding":"CVPR 2017 7","authors":["Eric Tzeng","Judy Hoffman","Kate Saenko","Trevor Darrell"],"abstract":"Adversarial learning methods are a promising approach to training robust deep\nnetworks, and can generate complex samples across diverse domains. They also\ncan improve recognition despite the presence of domain shift or dataset bias:\nseveral adversarial approaches to unsupervised domain adaptation have recently\nbeen introduced, which reduce the difference between the training and test\ndomain distributions and thus improve generalization performance. Prior\ngenerative approaches show compelling visualizations, but are not optimal on\ndiscriminative tasks and can be limited to smaller shifts. Prior discriminative\napproaches could handle larger domain shifts, but imposed tied weights on the\nmodel and did not exploit a GAN-based loss. We first outline a novel\ngeneralized framework for adversarial adaptation, which subsumes recent\nstate-of-the-art approaches as special cases, and we use this generalized view\nto better relate the prior approaches. 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We show that ADDA is more effective yet considerably\nsimpler than competing domain-adversarial methods, and demonstrate the promise\nof our approach by exceeding state-of-the-art unsupervised adaptation results\non standard cross-domain digit classification tasks and a new more difficult\ncross-modality object classification task.","url_abs":"http://arxiv.org/abs/1702.05464v1","url_pdf":"http://arxiv.org/pdf/1702.05464v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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