{"action":"opened","issue":{"url":"https://api.github.com/repos/leo-p/papers/issues/22","repository_url":"https://api.github.com/repos/leo-p/papers","labels_url":"https://api.github.com/repos/leo-p/papers/issues/22/labels{/name}","comments_url":"https://api.github.com/repos/leo-p/papers/issues/22/comments","events_url":"https://api.github.com/repos/leo-p/papers/issues/22/events","html_url":"https://github.com/leo-p/papers/issues/22","id":230483930,"number":22,"title":"Generate To Adapt - Aligning Domains using Generative Adversarial Networks","user":{"login":"leo-p","id":17261080,"avatar_url":"https://avatars3.githubusercontent.com/u/17261080?v=3","gravatar_id":"","url":"https://api.github.com/users/leo-p","html_url":"https://github.com/leo-p","followers_url":"https://api.github.com/users/leo-p/followers","following_url":"https://api.github.com/users/leo-p/following{/other_user}","gists_url":"https://api.github.com/users/leo-p/gists{/gist_id}","starred_url":"https://api.github.com/users/leo-p/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/leo-p/subscriptions","organizations_url":"https://api.github.com/users/leo-p/orgs","repos_url":"https://api.github.com/users/leo-p/repos","events_url":"https://api.github.com/users/leo-p/events{/privacy}","received_events_url":"https://api.github.com/users/leo-p/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-05-22T18:44:31Z","updated_at":"2017-05-22T18:44:31Z","closed_at":null,"body":"https://arxiv.org/pdf/1704.01705v1.pdf\r\n\r\nVisual Domain adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial framework. This is in contrast to methods which use an adversarial framework for realistic data generation and retraining deep models with such data. We show the strength and generality of our method by performing experiments on three different tasks: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Face recognition using the Celebrity Frontal Profile (CFP) dataset."}}
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