{"action":"created","issue":{"url":"https://api.github.com/repos/unizard/AwesomeArxiv/issues/198","repository_url":"https://api.github.com/repos/unizard/AwesomeArxiv","labels_url":"https://api.github.com/repos/unizard/AwesomeArxiv/issues/198/labels{/name}","comments_url":"https://api.github.com/repos/unizard/AwesomeArxiv/issues/198/comments","events_url":"https://api.github.com/repos/unizard/AwesomeArxiv/issues/198/events","html_url":"https://github.com/unizard/AwesomeArxiv/issues/198","id":332225240,"node_id":"MDU6SXNzdWUzMzIyMjUyNDA=","number":198,"title":"[2018.06.14] AutoAugment: Learning Augmentation Policies from Data","user":{"login":"unizard","id":11758940,"node_id":"MDQ6VXNlcjExNzU4OTQw","avatar_url":"https://avatars1.githubusercontent.com/u/11758940?v=4","gravatar_id":"","url":"https://api.github.com/users/unizard","html_url":"https://github.com/unizard","followers_url":"https://api.github.com/users/unizard/followers","following_url":"https://api.github.com/users/unizard/following{/other_user}","gists_url":"https://api.github.com/users/unizard/gists{/gist_id}","starred_url":"https://api.github.com/users/unizard/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/unizard/subscriptions","organizations_url":"https://api.github.com/users/unizard/orgs","repos_url":"https://api.github.com/users/unizard/repos","events_url":"https://api.github.com/users/unizard/events{/privacy}","received_events_url":"https://api.github.com/users/unizard/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-14T02:04:00Z","updated_at":"2018-06-14T02:21:30Z","closed_at":null,"author_association":"OWNER","body":"Institute: Google Brain\r\nURL: https://arxiv.org/pdf/1805.09501.pdf\r\nKeyword: Data Augmentation, AutoML, ReinforceLearning\r\nInterest: 5\r\n\r\nGoogleBlog: https://ai.googleblog.com/2018/06/improving-deep-learning-performance.html\r\n\r\n**Summary**\r\nIn this paper, we take a closer look at data augmentation for images, and describe a simple procedure called AutoAugment to search for improved data augmentation policies. On ImageNet, we attain a Top-1 accuracy of 83.54%. On CIFAR-10, we achieve an error rate of 1.48%, which is 0.65% better than the previous state-of-the-art. On reduced data settings, AutoAugment performs comparably to semi-supervised methods without using any unlabeled examples.\r\n\r\n#역시 생각만하면 지는군 #멋져요 구글 브레인 짱짱맨\r\n"},"comment":{"url":"https://api.github.com/repos/unizard/AwesomeArxiv/issues/comments/397146198","html_url":"https://github.com/unizard/AwesomeArxiv/issues/198#issuecomment-397146198","issue_url":"https://api.github.com/repos/unizard/AwesomeArxiv/issues/198","id":397146198,"node_id":"MDEyOklzc3VlQ29tbWVudDM5NzE0NjE5OA==","user":{"login":"unizard","id":11758940,"node_id":"MDQ6VXNlcjExNzU4OTQw","avatar_url":"https://avatars1.githubusercontent.com/u/11758940?v=4","gravatar_id":"","url":"https://api.github.com/users/unizard","html_url":"https://github.com/unizard","followers_url":"https://api.github.com/users/unizard/followers","following_url":"https://api.github.com/users/unizard/following{/other_user}","gists_url":"https://api.github.com/users/unizard/gists{/gist_id}","starred_url":"https://api.github.com/users/unizard/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/unizard/subscriptions","organizations_url":"https://api.github.com/users/unizard/orgs","repos_url":"https://api.github.com/users/unizard/repos","events_url":"https://api.github.com/users/unizard/events{/privacy}","received_events_url":"https://api.github.com/users/unizard/received_events","type":"User","site_admin":false},"created_at":"2018-06-14T02:21:30Z","updated_at":"2018-06-14T02:21:30Z","author_association":"OWNER","body":"\r\nRelated Work\r\n- [Automatic Dataset Augmentation](https://arxiv.org/pdf/1708.08201v1.pdf), CoRR 2017. MSRA\r\n    - https://auto-da.github.io/ "}}
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