{"blog_url":"https://ai-de-seikei.hatenablog.com/","provider_name":"Hatena Blog","version":"1.0","blog_title":"AI\u3046\u3049\uff0d\uff0d\uff01(ai-wo-katsuyo-shitai !)","url":"https://ai-de-seikei.hatenablog.com/entry/2022/01/16/173639","width":"100%","description":"SENet\u3067\u300cself-attention\u300d\u307e\u3067\u3064\u306a\u3052\u308b\u306e\u306f\u3001\u3055\u3059\u304c\u306b\uff08\u7b46\u8005\u306e\uff09\u8a71\u306e\u76db\u308a\u904e\u304e\u304b\u3002 \u4ee5\u4e0b\u306e\u8ad6\u6587\u3002 Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141). attention\u3084\u3001self-attention\u306b\u3064\u3044\u3066\u3069\u306e\u3088\u3046\u306a\u8a18\u8f09\u306b\u306a\u3063\u3066\u3044\u308b\u304b\u3002 \u8ad6\u6587\u629c\u7c8b1(attention) \u203b\u3053\u3053\u306f\u3001SENet\u306b\u95a2\u3059\u308b\u8a18\u2026","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fai-de-seikei.hatenablog.com%2Fentry%2F2022%2F01%2F16%2F173639\" title=\"SENet\u3067\u300cself-attention\u300d\u307e\u3067\u3064\u306a\u3052\u308b\u306e\u306f\u3001\u3055\u3059\u304c\u306b\uff08\u7b46\u8005\u306e\uff09\u8a71\u306e\u76db\u308a\u904e\u304e\u304b\u3002 - AI\u3046\u3049\uff0d\uff0d\uff01(ai-wo-katsuyo-shitai !)\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","height":"190","author_name":"fanta_orange_grape","title":"SENet\u3067\u300cself-attention\u300d\u307e\u3067\u3064\u306a\u3052\u308b\u306e\u306f\u3001\u3055\u3059\u304c\u306b\uff08\u7b46\u8005\u306e\uff09\u8a71\u306e\u76db\u308a\u904e\u304e\u304b\u3002","author_url":"https://blog.hatena.ne.jp/fanta_orange_grape/","categories":[],"image_url":null,"type":"rich","provider_url":"https://hatena.blog","published":"2022-01-16 17:36:39"}