{"description":"\u8aad\u3093\u3060\u306e\u3067\u81ea\u5206\u306e\u6574\u7406\u306e\u305f\u3081\u306b\u307e\u3068\u3081\u307e\u3059\u3002 [1703.05921] Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery \u5c0e\u5165 \u6271\u3046\u554f\u984c \u554f\u984c\u610f\u8b58 \u30e1\u30a4\u30f3\u30a2\u30a4\u30c7\u30a2 \u7406\u8ad6 \u5927\u7b4b \u5b9a\u5f0f\u5316\u30fb\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0 GAN \u65b0\u3057\u3044\u753b\u50cf\u3092\u6f5c\u5728\u7a7a\u9593\u306b\u5199\u50cf Residual Loss Discrimination Loss \u5b9f\u9a13 \u7d50\u8ad6","type":"rich","author_name":"aotamasaki","height":"190","provider_name":"Hatena Blog","width":"100%","author_url":"https://blog.hatena.ne.jp/aotamasaki/","published":"2018-04-14 21:29:48","blog_url":"https://aotamasaki.hatenablog.com/","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Faotamasaki.hatenablog.com%2Fentry%2F2018%2F04%2F14%2F212948\" title=\"\u8ad6\u6587\u8aad\u307f Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery - \u5b66\u7fd2\u3059\u308b\u5929\u7136\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","version":"1.0","blog_title":"\u5b66\u7fd2\u3059\u308b\u5929\u7136\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8","url":"https://aotamasaki.hatenablog.com/entry/2018/04/14/212948","provider_url":"https://hatena.blog","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/a/aotamasaki/20180414/20180414211624.png","categories":["\u6df1\u5c64\u5b66\u7fd2","\u6a5f\u68b0\u5b66\u7fd2"],"title":"\u8ad6\u6587\u8aad\u307f Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery"}