{"categories":[],"width":"100%","url":"https://fanta-orange-grape.hatenablog.com/entry/2021/07/23/145522","title":"\u82f1\u8a9e\u3002\u5358\u8a9e\u3082\u6587\u6cd5\u3082\u7c21\u5358\u306a\u306e\u306b\u3001\u82f1\u8a9e\u306e\u610f\u5473\u304c\u6b63\u78ba\u306b\u306f\u982d\u306b\u5165\u308a\u307e\u3057\u3047\u30fc\u3093\u3002","provider_name":"Hatena Blog","published":"2021-07-23 14:55:22","provider_url":"https://hatena.blog","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Ffanta-orange-grape.hatenablog.com%2Fentry%2F2021%2F07%2F23%2F145522\" title=\"\u82f1\u8a9e\u3002\u5358\u8a9e\u3082\u6587\u6cd5\u3082\u7c21\u5358\u306a\u306e\u306b\u3001\u82f1\u8a9e\u306e\u610f\u5473\u304c\u6b63\u78ba\u306b\u306f\u982d\u306b\u5165\u308a\u307e\u3057\u3047\u30fc\u3093\u3002 - fanta_orange_grape\u306e\uff08\u65e5\u8a18\u3068\u3044\u3046\u3088\u308a\uff09\u8a18\u4e8b\u306e\u3064\u3082\u308a\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","type":"rich","height":"190","blog_title":"fanta_orange_grape\u306e\uff08\u65e5\u8a18\u3068\u3044\u3046\u3088\u308a\uff09\u8a18\u4e8b\u306e\u3064\u3082\u308a","image_url":null,"author_name":"fanta_orange_grape","version":"1.0","author_url":"https://blog.hatena.ne.jp/fanta_orange_grape/","blog_url":"https://fanta-orange-grape.hatenablog.com/","description":"\u4f8b\u65871 \u82f1\u6587 Domain adaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning from a source data distribution a well performing model on a different (but related) target data distribution. For instance, one of the tasks of the common spam fi\u2026"}