{"action":"opened","issue":{"url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/480","repository_url":"https://api.github.com/repos/arXivTimes/arXivTimes","labels_url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/480/labels{/name}","comments_url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/480/comments","events_url":"https://api.github.com/repos/arXivTimes/arXivTimes/issues/480/events","html_url":"https://github.com/arXivTimes/arXivTimes/issues/480","id":270234804,"number":480,"title":"On Pre-Trained Image Features and Synthetic Images for Deep Learning","user":{"login":"icoxfog417","id":544269,"avatar_url":"https://avatars0.githubusercontent.com/u/544269?v=4","gravatar_id":"","url":"https://api.github.com/users/icoxfog417","html_url":"https://github.com/icoxfog417","followers_url":"https://api.github.com/users/icoxfog417/followers","following_url":"https://api.github.com/users/icoxfog417/following{/other_user}","gists_url":"https://api.github.com/users/icoxfog417/gists{/gist_id}","starred_url":"https://api.github.com/users/icoxfog417/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/icoxfog417/subscriptions","organizations_url":"https://api.github.com/users/icoxfog417/orgs","repos_url":"https://api.github.com/users/icoxfog417/repos","events_url":"https://api.github.com/users/icoxfog417/events{/privacy}","received_events_url":"https://api.github.com/users/icoxfog417/received_events","type":"User","site_admin":false},"labels":[{"id":526884338,"url":"https://api.github.com/repos/arXivTimes/arXivTimes/labels/CNN","name":"CNN","color":"fbca04","default":false},{"id":469035224,"url":"https://api.github.com/repos/arXivTimes/arXivTimes/labels/ComputerVision","name":"ComputerVision","color":"fef2c0","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-11-01T08:39:50Z","updated_at":"2017-11-01T08:39:50Z","closed_at":null,"author_association":"OWNER","body":"## 一言でいうと\r\n\r\n物体検知のモデルで合成データを学習に使う方法の提案。実物で学習された事前学習済みの重みを固定した上で、合成データのみで学習すると実物のみで学習と同等の結果が得られたという(合成のみでゼロから学習すると低精度)。なお、合成データは背景画像に3Dモデルを置くという手法で作成している。\r\n\r\n### 論文リンク\r\n\r\nhttps://arxiv.org/abs/1710.10710\r\n\r\n### 著者/所属機関\r\n\r\nStefan Hinterstoisser, Vincent Lepetit, Paul Wohlhart, Kurt Konolige\r\n\r\n* X\r\n* Universite de Bordeaux\r\n\r\n### 投稿日付(yyyy/MM/dd)\r\n\r\n2017/10/29\r\n\r\n## 概要\r\n\r\n## 新規性・差分\r\n\r\n## 手法\r\n\r\n合成データの作成方法\r\n\r\n![image](https://user-images.githubusercontent.com/544269/32266597-54325550-bf2b-11e7-9578-2f7112403545.png)\r\n\r\n\r\n## 結果\r\n\r\n![image](https://user-images.githubusercontent.com/544269/32266683-a7975ba0-bf2b-11e7-9e3d-d81ee757f097.png)\r\n\r\n\r\n## コメント\r\n\r\n"}}
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