{"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/unsupervised-domain-adaptation-by","title":"Unsupervised Domain Adaptation by Backpropagation","arxiv_id":"1409.7495","date":"2014-09-26","proceeding":null,"authors":["Yaroslav Ganin","Victor Lempitsky"],"abstract":"Top-performing deep architectures are trained on massive amounts of labeled\ndata. In the absence of labeled data for a certain task, domain adaptation\noften provides an attractive option given that labeled data of similar nature\nbut from a different domain (e.g. synthetic images) are available. Here, we\npropose a new approach to domain adaptation in deep architectures that can be\ntrained on large amount of labeled data from the source domain and large amount\nof unlabeled data from the target domain (no labeled target-domain data is\nnecessary).\n  As the training progresses, the approach promotes the emergence of \"deep\"\nfeatures that are (i) discriminative for the main learning task on the source\ndomain and (ii) invariant with respect to the shift between the domains. We\nshow that this adaptation behaviour can be achieved in almost any feed-forward\nmodel by augmenting it with few standard layers and a simple new gradient\nreversal layer. The resulting augmented architecture can be trained using\nstandard backpropagation.\n  Overall, the approach can be implemented with little effort using any of the\ndeep-learning packages. The method performs very well in a series of image\nclassification experiments, achieving adaptation effect in the presence of big\ndomain shifts and outperforming previous state-of-the-art on Office datasets.","url_abs":"http://arxiv.org/abs/1409.7495v2","url_pdf":"http://arxiv.org/pdf/1409.7495v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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