{"id":12977,"date":"2024-08-19T16:57:19","date_gmt":"2024-08-19T21:57:19","guid":{"rendered":"http:\/\/skimai.com\/?p=12977"},"modified":"2024-08-19T16:57:19","modified_gmt":"2024-08-19T21:57:19","slug":"%e6%95%b0%e7%99%ba%e5%ad%a6%e7%bf%92%e3%81%ab%e9%96%a2%e3%81%99%e3%82%8b%e7%a0%94%e7%a9%b6%e8%ab%96%e6%96%87%e3%83%88%e3%83%83%e3%83%975","status":"publish","type":"post","link":"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/","title":{"rendered":"\u6570\u6483\u3061\u3083\u5f53\u305f\u308b\u306e\u7814\u7a76\u8ad6\u6587\u30c8\u30c3\u30d75"},"content":{"rendered":"<p>\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u306b\u304a\u3051\u308b\u91cd\u8981\u306a\u7814\u7a76\u5206\u91ce\u3068\u3057\u3066\u6d6e\u4e0a\u3057\u3066\u304a\u308a\u3001\u9650\u3089\u308c\u305f\u30e9\u30d9\u30eb\u4ed8\u304d\u4f8b\u304b\u3089\u5b66\u7fd2\u3067\u304d\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u958b\u767a\u3092\u76ee\u6307\u3057\u3066\u3044\u308b\u3002\u3053\u306e\u80fd\u529b\u306f\u3001\u30c7\u30fc\u30bf\u304c\u4e4f\u3057\u304b\u3063\u305f\u308a\u3001\u9ad8\u4fa1\u3067\u3042\u3063\u305f\u308a\u3001\u6642\u9593\u304c\u304b\u304b\u3063\u305f\u308a\u3059\u308b\u591a\u304f\u306e\u5b9f\u4e16\u754c\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u3068\u3063\u3066\u4e0d\u53ef\u6b20\u3067\u3042\u308b\u3002 <\/p>\n\n\n<p>\u672c\u8b1b\u6f14\u3067\u306f\u3001\u5b9f\u88c5\u3055\u308c\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u5c11\u6570\u70b9\u5b66\u7fd2\u306e\u5206\u91ce\u3092\u5927\u304d\u304f\u524d\u9032\u3055\u305b\u305f\u30015\u3064\u306e\u91cd\u8981\u306a\u7814\u7a76\u8ad6\u6587\u3092\u7d39\u4ecb\u3059\u308b\u3002\u3053\u308c\u3089\u306e\u8ad6\u6587\u306f\u3001\u65b0\u3057\u3044\u30a2\u30d7\u30ed\u30fc\u30c1\u3001\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3001\u8a55\u4fa1\u30d7\u30ed\u30c8\u30b3\u30eb\u3092\u7d39\u4ecb\u3057\u3001\u3053\u306e\u56f0\u96e3\u306a\u9818\u57df\u3067\u53ef\u80fd\u306a\u3053\u3068\u306e\u9650\u754c\u3092\u62bc\u3057\u5e83\u3052\u3066\u3044\u308b\u3002\u3053\u308c\u3089\u306e\u8ca2\u732e\u3092\u691c\u8a3c\u3059\u308b\u3053\u3068\u3067\u3001\u5c11\u6570\u70b9\u5b66\u7fd2\u306e\u73fe\u72b6\u3092\u5305\u62ec\u7684\u306b\u6982\u89b3\u3057\u3001\u3053\u306e\u30a8\u30ad\u30b5\u30a4\u30c6\u30a3\u30f3\u30b0\u306a\u5206\u91ce\u3067\u306e\u3055\u3089\u306a\u308b\u7814\u7a76\u3092\u4fc3\u3057\u305f\u3044\u3002<\/p>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u76ee\u6b21<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"\u30c8\u30b0\u30eb\u76ee\u6b21\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">\u30c8\u30b0\u30eb<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/#1_Matching_Networks_for_One_Shot_Learning_Vinyals_et_al_2016\" >1.\u4e00\u767a\u5b66\u7fd2\u306e\u305f\u3081\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (Vinyals et al., 2016)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/#2_Prototypical_Networks_for_Few-shot_Learning_Snell_et_al_2017\" >2.\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u305f\u3081\u306e\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08Snell et al.\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/#3_Learning_to_Compare_Relation_Network_for_Few-Shot_Learning_Sung_et_al_2018\" >3.\u6bd4\u8f03\u3059\u308b\u5b66\u7fd2\uff1a\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u305f\u3081\u306e\u95a2\u4fc2\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08Sung et al.\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/#4_A_Closer_Look_at_Few-shot_Classification_Chen_et_al_2019\" >4.\u30d5\u30e5\u30fc\u30b7\u30e7\u30c3\u30c8\u5206\u985e\u306b\u8feb\u308b\uff08Chen et al.\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/#5_Meta-Baseline_Exploring_Simple_Meta-Learning_for_Few-Shot_Learning_Chen_et_al_2021\" >5.\u30e1\u30bf\u30fb\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u305f\u3081\u306e\u30b7\u30f3\u30d7\u30eb\u306a\u30e1\u30bf\u5b66\u7fd2\u306e\u63a2\u6c42\uff08Chen et al.\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/skimai.com\/ja\/top-5-research-papers-on-few-shot-learning\/#The_Evolution_of_Few-Shot_Learning_Simplicity_Insight_and_Future_Directions\" >\u6570\u6483\u3061\u3083\u5f53\u305f\u308b\u5b66\u7fd2\u306e\u9032\u5316\uff1a\u30b7\u30f3\u30d7\u30eb\u3055\u3001\u6d1e\u5bdf\u529b\u3001\u305d\u3057\u3066\u4eca\u5f8c\u306e\u65b9\u5411\u6027<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Matching_Networks_for_One_Shot_Learning_Vinyals_et_al_2016\"><\/span>1. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1606.04080v2\">\u4e00\u767a\u5b66\u7fd2\u306e\u305f\u3081\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08Vinyals et al.\uff09<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/57a2b756-e57b-4201-810c-46988bbf2482.png\" alt=\"\u4e00\u767a\u5b66\u7fd2\u306e\u7814\u7a76\u8ad6\u6587\" \/>\n<\/figure>\n\n\n<p>\u30de\u30c3\u30c1\u30f3\u30b0\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u3001\u8a18\u61b6\u3068\u6ce8\u610f\u306e\u30e1\u30ab\u30cb\u30ba\u30e0\u304b\u3089\u30d2\u30f3\u30c8\u3092\u5f97\u305f\u3001\u4e00\u767a\u5b66\u7fd2\u3078\u306e\u753b\u671f\u7684\u306a\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u7d39\u4ecb\u3057\u305f\u3002\u3053\u306e\u8ad6\u6587\u306e\u91cd\u8981\u306a\u9769\u65b0\u70b9\u306f\u30de\u30c3\u30c1\u30f3\u30b0\u6a5f\u80fd\u3067\u3042\u308a\u3001\u30af\u30a8\u30ea\u30fc\u4f8b\u3068\u30e9\u30d9\u30eb\u4ed8\u3051\u3055\u308c\u305f\u30b5\u30dd\u30fc\u30c8\u4f8b\u3092\u6bd4\u8f03\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3046\u3002<\/p>\n\n\n<p>\u8457\u8005\u3089\u306f\u3001\u8a13\u7df4\u4e2d\u306b\u6570\u30b7\u30e7\u30c3\u30c8\u306e\u30b7\u30ca\u30ea\u30aa\u3092\u6a21\u5023\u3059\u308b\u30a8\u30d4\u30bd\u30fc\u30c9\u8a13\u7df4\u30ec\u30b8\u30fc\u30e0\u3092\u63d0\u6848\u3057\u3001\u30e2\u30c7\u30eb\u304c\u308f\u305a\u304b\u6570\u4f8b\u304b\u3089\u5b66\u7fd2\u65b9\u6cd5\u3092\u5b66\u3076\u3053\u3068\u3092\u53ef\u80fd\u306b\u3057\u305f\u3002\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306f\u3001\u6570\u5c11\u306a\u3044\u4f8b\u304b\u3089\u5b66\u7fd2\u3059\u308b\u65b9\u6cd5\u3092\u30e2\u30c7\u30eb\u306b\u5b66\u7fd2\u3055\u305b\u308b\u3053\u3068\u3067\u3001\u6570\u5c11\u306a\u3044\u4f8b\u304b\u3089\u5b66\u7fd2\u3059\u308b\u30e1\u30bf\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3078\u306e\u9053\u3092\u958b\u3044\u305f\u3002Matching Networks\u306f\u3001Omniglot\u3068miniImageNet\u306e\u4e21\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u76ee\u899a\u307e\u3057\u3044\u6027\u80fd\u3092\u793a\u3057\u3001\u5c11\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u6cd5\u306e\u65b0\u305f\u306a\u57fa\u6e96\u3092\u6253\u3061\u7acb\u3066\u305f\u3002<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Prototypical_Networks_for_Few-shot_Learning_Snell_et_al_2017\"><\/span>2. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1703.05175v2\">\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u305f\u3081\u306e\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08Snell et al.\uff09<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/7e925e4a-0fab-467c-8fec-181feb4ab18c.png\" alt=\"\u6570\u6483\u3061\u3083\u5f53\u305f\u308b\u306e\u7814\u7a76\u8ad6\u6587\" \/>\n<\/figure>\n\n\n<p>\u30de\u30c3\u30c1\u30f3\u30b0\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u6210\u529f\u306b\u57fa\u3065\u304d\u3001\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u3001\u3088\u308a\u30b7\u30f3\u30d7\u30eb\u3067\u52b9\u679c\u7684\u306a\u5c11\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u5c0e\u5165\u3057\u305f\u3002\u91cd\u8981\u306a\u30a2\u30a4\u30c7\u30a2\u306f\u3001\u30af\u30e9\u30b9\u304c1\u3064\u306e\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7-\u305d\u306e\u30af\u30e9\u30b9\u306e\u57cb\u3081\u8fbc\u307f\u30b5\u30dd\u30fc\u30c8\u4f8b\u306e\u5e73\u5747-\u3067\u8868\u73fe\u3067\u304d\u308b\u8a08\u91cf\u7a7a\u9593\u3092\u5b66\u7fd2\u3059\u308b\u3053\u3068\u3067\u3042\u308b\u3002<\/p>\n\n\n<p>\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u3001\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u4ee3\u308f\u308a\u306b\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2\u3092\u4f7f\u3046\u304c\u3001\u8457\u8005\u3089\u306f\u30d6\u30ec\u30b0\u30de\u30f3\u30fb\u30c0\u30a4\u30d0\u30fc\u30b8\u30a7\u30f3\u30b9\u3068\u3057\u3066\u3088\u308a\u9069\u5207\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u308b\u3002\u3053\u306e\u9078\u629e\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u306e\u660e\u78ba\u306a\u78ba\u7387\u7684\u89e3\u91c8\u304c\u53ef\u80fd\u306b\u306a\u308b\u3002\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30b7\u30f3\u30d7\u30eb\u3055\u3068\u6709\u52b9\u6027\u306f\u3001\u305d\u306e\u5f8c\u306e\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u7814\u7a76\u306e\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u3068\u3057\u3066\u4eba\u6c17\u304c\u3042\u308a\u3001\u3057\u3070\u3057\u3070\u3088\u308a\u8907\u96d1\u306a\u624b\u6cd5\u3092\u51cc\u99d5\u3059\u308b\u3002<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Learning_to_Compare_Relation_Network_for_Few-Shot_Learning_Sung_et_al_2018\"><\/span>3. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1711.06025v2\">\u6bd4\u8f03\u3059\u308b\u5b66\u7fd2\uff1a\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u305f\u3081\u306e\u95a2\u4fc2\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08Sung et al.\uff09<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/050788bb-e54a-4bad-98f0-94b622afa50d.png\" alt=\"\u6570\u6483\u3061\u3083\u5f53\u305f\u308b\u306e\u7814\u7a76\u8ad6\u6587\" \/>\n<\/figure>\n\n\n<p>Relation Networks\u306f\u3001\u5b66\u7fd2\u53ef\u80fd\u306a\u95a2\u4fc2\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u5c0e\u5165\u3059\u308b\u3053\u3068\u3067\u3001\u3053\u308c\u307e\u3067\u306e\u624b\u6cd5\u306e\u30e1\u30c8\u30ea\u30c3\u30af\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4e00\u6b69\u9032\u3081\u305f\u3002\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u8ddd\u96e2\u3084\u30b3\u30b5\u30a4\u30f3\u985e\u4f3c\u5ea6\u306e\u3088\u3046\u306a\u56fa\u5b9a\u3055\u308c\u305f\u30e1\u30c8\u30ea\u30c3\u30af\u3092\u4f7f\u3046\u4ee3\u308f\u308a\u306b\u3001Relation Networks\u306f\u30af\u30a8\u30ea\u3068\u30b5\u30dd\u30fc\u30c8\u306e\u4f8b\u3092\u67d4\u8edf\u306b\u6bd4\u8f03\u3059\u308b\u3053\u3068\u3092\u5b66\u7fd2\u3059\u308b\u3002<\/p>\n\n\n<p>\u30ea\u30ec\u30fc\u30b7\u30e7\u30f3\u30e2\u30b8\u30e5\u30fc\u30eb\u306f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u3057\u3066\u5b9f\u88c5\u3055\u308c\u3001\u30af\u30a8\u30ea\u3068\u30b5\u30dd\u30fc\u30c8\u4f8b\u306e\u7279\u5fb4\u3092\u9023\u7d50\u3057\u305f\u3082\u306e\u3092\u5165\u529b\u3068\u3057\u3001\u30ea\u30ec\u30fc\u30b7\u30e7\u30f3\u30b9\u30b3\u30a2\u3092\u51fa\u529b\u3059\u308b\u3002\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u306f\u7279\u5b9a\u306e\u30bf\u30b9\u30af\u3068\u30c7\u30fc\u30bf\u5206\u5e03\u306b\u5408\u308f\u305b\u305f\u6bd4\u8f03\u6307\u6a19\u3092\u5b66\u7fd2\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002\u95a2\u4fc2\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u3001\u69d8\u3005\u306a\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u306b\u304a\u3044\u3066\u5f37\u529b\u306a\u6027\u80fd\u3092\u793a\u3057\u3001\u6bd4\u8f03\u5b66\u7fd2\u306e\u5a01\u529b\u3092\u5b9f\u8a3c\u3057\u305f\u3002<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_A_Closer_Look_at_Few-shot_Classification_Chen_et_al_2019\"><\/span>4. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/1904.04232v2\">\u30d5\u30e5\u30fc\u30b7\u30e7\u30c3\u30c8\u5206\u985e\u306b\u8feb\u308b\uff08Chen et al.\uff09<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/badbf4a2-f014-40ff-a525-f2e572c86494.png\" alt=\"\u6570\u6483\u3061\u3083\u5f53\u305f\u308b\u306e\u7814\u7a76\u8ad6\u6587\" \/>\n<\/figure>\n\n\n<p>\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001\u65e2\u5b58\u306e\u6570\u767a\u5b66\u7fd2\u6cd5\u3092\u5305\u62ec\u7684\u306b\u5206\u6790\u3057\u3001\u3053\u306e\u5206\u91ce\u306b\u304a\u3051\u308b\u3044\u304f\u3064\u304b\u306e\u4e00\u822c\u7684\u306a\u4eee\u5b9a\u306b\u6311\u6226\u3057\u305f\u3002\u8457\u8005\u3089\u306f\u3001\u9069\u5207\u306b\u8a13\u7df4\u3055\u308c\u305f\u5834\u5408\u3001\u3088\u308a\u8907\u96d1\u306a\u30e1\u30bf\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u6027\u80fd\u306b\u5339\u6575\u3059\u308b\u304b\u3001\u305d\u308c\u3092\u4e0a\u56de\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u5358\u7d14\u306a\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u30e2\u30c7\u30eb\u3092\u63d0\u6848\u3057\u305f\u3002<\/p>\n\n\n<p>\u3053\u306e\u7814\u7a76\u304b\u3089\u306e\u91cd\u8981\u306a\u6d1e\u5bdf\u306f\u3001\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306b\u304a\u3051\u308b\u7279\u5fb4\u30d0\u30c3\u30af\u30dc\u30fc\u30f3\u3068\u5b66\u7fd2\u6226\u7565\u306e\u91cd\u8981\u6027\u3067\u3042\u308b\u3002\u8457\u8005\u3089\u306f\u3001\u5168\u3066\u306e\u30d9\u30fc\u30b9\u30af\u30e9\u30b9\u3067\u8a13\u7df4\u3055\u308c\u305f\u6a19\u6e96\u7684\u306a\u5206\u985e\u5668\u3068\u3001\u305d\u308c\u306b\u7d9a\u304f\u65b0\u898f\u30af\u30e9\u30b9\u3067\u306e\u6700\u8fd1\u508d\u5206\u985e\u304c\u975e\u5e38\u306b\u52b9\u679c\u7684\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u305f\u3002\u672c\u8ad6\u6587\u306f\u3001\u7814\u7a76\u8005\u306b\u5bfe\u3057\u3001\u6570\u500b\u5358\u4f4d\u306e\u5b66\u7fd2\u7814\u7a76\u306b\u304a\u3051\u308b\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u3068\u8a55\u4fa1\u30d7\u30ed\u30c8\u30b3\u30eb\u3092\u6ce8\u610f\u6df1\u304f\u691c\u8a0e\u3059\u308b\u3088\u3046\u4fc3\u3057\u305f\u3002<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Meta-Baseline_Exploring_Simple_Meta-Learning_for_Few-Shot_Learning_Chen_et_al_2021\"><\/span>5. <a rel=\"noopener noreferrer\" href=\"https:\/\/arxiv.org\/pdf\/2003.04390v4\">\u30e1\u30bf\u30fb\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u306e\u305f\u3081\u306e\u30b7\u30f3\u30d7\u30eb\u306a\u30e1\u30bf\u5b66\u7fd2\u306e\u63a2\u6c42\uff08Chen et al.\uff09<\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<figure class=\"wp-block-image\">\n<img decoding=\"async\" src=\"http:\/\/skimai.com\/wp-content\/uploads\/2024\/08\/ed044b3e-4736-4107-a7b1-a2706100e01c.png\" alt=\"\u30e1\u30bf\u30e9\u30fc\u30cb\u30f3\u30b0\u7814\u7a76\u8ad6\u6587\" \/>\n<\/figure>\n\n\n<p>A Closer Look at Few-shot Classification \"\u306e\u6d1e\u5bdf\u306b\u57fa\u3065\u304d\u3001Meta-Baseline\u306f\u30b7\u30f3\u30d7\u30eb\u304b\u3064\u975e\u5e38\u306b\u52b9\u679c\u7684\u306a\u30e1\u30bf\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u63d0\u6848\u3059\u308b\u3002\u3053\u306e\u65b9\u6cd5\u306f\u3001\u57fa\u672c\u30af\u30e9\u30b9\u306b\u5bfe\u3059\u308b\u6a19\u6e96\u7684\u306a\u4e8b\u524d\u5b66\u7fd2\u3068\u3001\u5c11\u6570\u30b7\u30e7\u30c3\u30c8\u30bf\u30b9\u30af\u306e\u305f\u3081\u306b\u30e2\u30c7\u30eb\u3092\u5fae\u8abf\u6574\u3059\u308b\u30e1\u30bf\u5b66\u7fd2\u6bb5\u968e\u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u3044\u308b\u3002<\/p>\n\n\n<p>\u8457\u8005\u3089\u306f\u3001\u6a19\u6e96\u7684\u306a\u8a13\u7df4\u3068\u30e1\u30bf\u5b66\u7fd2\u306e\u76ee\u7684\u9593\u306e\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u306e\u8a73\u7d30\u306a\u5206\u6790\u3092\u63d0\u4f9b\u3057\u3066\u3044\u308b\u3002\u30e1\u30bf\u5b66\u7fd2\u306f\u8a13\u7df4\u5206\u5e03\u306e\u6027\u80fd\u3092\u5411\u4e0a\u3055\u305b\u308b\u304c\u3001\u65b0\u898f\u30af\u30e9\u30b9\u3078\u306e\u6c4e\u5316\u3092\u5bb3\u3059\u308b\u5834\u5408\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u308b\u3002Meta-Baseline\u306f\u3001\u6a19\u6e96\u7684\u306a\u6570\u30b7\u30e7\u30c3\u30c8\u5b66\u7fd2\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u306b\u304a\u3044\u3066\u6700\u5148\u7aef\u306e\u6027\u80fd\u3092\u9054\u6210\u3057\u3001\u9069\u5207\u306b\u8a2d\u8a08\u3001\u5206\u6790\u3055\u308c\u305f\u5834\u5408\u3001\u5358\u7d14\u306a\u30a2\u30d7\u30ed\u30fc\u30c1\u304c\u975e\u5e38\u306b\u52b9\u679c\u7684\u3067\u3042\u308b\u3053\u3068\u3092\u5b9f\u8a3c\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Evolution_of_Few-Shot_Learning_Simplicity_Insight_and_Future_Directions\"><\/span>\u6570\u6483\u3061\u3083\u5f53\u305f\u308b\u5b66\u7fd2\u306e\u9032\u5316\uff1a\u30b7\u30f3\u30d7\u30eb\u3055\u3001\u6d1e\u5bdf\u529b\u3001\u305d\u3057\u3066\u4eca\u5f8c\u306e\u65b9\u5411\u6027<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<p>\u3053\u308c\u30895\u3064\u306e\u753b\u671f\u7684\u306a\u8ad6\u6587\u306f\u3001\u5b66\u8853\u7814\u7a76\u3092\u524d\u9032\u3055\u305b\u305f\u3060\u3051\u3067\u306a\u304f\u3001\u4f01\u696dAI\u306b\u304a\u3051\u308b\u5c11\u6570\u70b9\u5b66\u7fd2\u306e\u5b9f\u7528\u5316\u3078\u306e\u9053\u3092\u958b\u3044\u305f\u3002\u30de\u30c3\u30c1\u30f3\u30b0\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304b\u3089\u30e1\u30bf\u30fb\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u306b\u81f3\u308b\u307e\u3067\u3001\u9650\u3089\u308c\u305f\u30c7\u30fc\u30bf\u304b\u3089\u5b66\u7fd2\u3067\u304d\u308b\u3001\u3088\u308a\u52b9\u7387\u7684\u3067\u9069\u5fdc\u6027\u306e\u9ad8\u3044AI\u30b7\u30b9\u30c6\u30e0\u3078\u306e\u9032\u5c55\u304c\u898b\u3089\u308c\u308b\u3002\u3053\u308c\u3089\u306e\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308a\u3001\u4f01\u696d\u306f\u3001\u7a00\u306a\u30a4\u30d9\u30f3\u30c8\u306e\u691c\u51fa\u3001\u30d1\u30fc\u30bd\u30ca\u30e9\u30a4\u30ba\u3055\u308c\u305f\u9867\u5ba2\u4f53\u9a13\u3001\u65b0\u3057\u3044AI\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u306e\u8fc5\u901f\u306a\u30d7\u30ed\u30c8\u30bf\u30a4\u30d4\u30f3\u30b0\u306a\u3069\u3001\u30c7\u30fc\u30bf\u304c\u4e4f\u3057\u304b\u3063\u305f\u308a\u5165\u624b\u306b\u30b3\u30b9\u30c8\u304c\u304b\u304b\u308b\u30b7\u30ca\u30ea\u30aa\u3067AI\u3092\u5c0e\u5165\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002 <\/p>\n\n\n<p>\u5f8c\u306e\u8ad6\u6587\u3067\u5f37\u8abf\u3055\u308c\u3066\u3044\u308b\u3088\u3046\u306b\u3001\u3088\u308a\u30b7\u30f3\u30d7\u30eb\u3067\u52b9\u679c\u7684\u306a\u30e2\u30c7\u30eb\u3092\u91cd\u8996\u3059\u308b\u59ff\u52e2\u306f\u3001\u89e3\u91c8\u53ef\u80fd\u3067\u4fdd\u5b88\u53ef\u80fd\u306aAI\u30b7\u30b9\u30c6\u30e0\u306b\u5bfe\u3059\u308b\u4f01\u696d\u306e\u30cb\u30fc\u30ba\u3068\u3088\u304f\u4e00\u81f4\u3057\u3066\u3044\u308b\u3002\u4f01\u696d\u304cAI\u3092\u901a\u3058\u3066\u7af6\u4e89\u4e0a\u306e\u512a\u4f4d\u6027\u3092\u8ffd\u6c42\u3057\u7d9a\u3051\u308b\u4e2d\u3001\u6700\u5c0f\u9650\u306e\u30c7\u30fc\u30bf\u3067\u65b0\u3057\u3044\u30bf\u30b9\u30af\u306b\u30e2\u30c7\u30eb\u3092\u8fc5\u901f\u306b\u9069\u5fdc\u3055\u305b\u308b\u80fd\u529b\u306f\u3001\u307e\u3059\u307e\u3059\u4fa1\u5024\u3092\u5897\u3057\u3066\u3044\u304f\u3060\u308d\u3046\u3002\u3053\u308c\u3089\u306e\u8ad6\u6587\u306e\u65c5\u306f\u3001\u30a8\u30f3\u30bf\u30fc\u30d7\u30e9\u30a4\u30baAI\u304c\u3088\u308a\u6a5f\u654f\u306b\u3001\u30b3\u30b9\u30c8\u52b9\u7387\u3088\u304f\u3001\u6025\u901f\u306b\u5909\u5316\u3059\u308b\u30d3\u30b8\u30cd\u30b9\u30cb\u30fc\u30ba\u306b\u5bfe\u5fdc\u3057\u3001\u6700\u7d42\u7684\u306b\u696d\u754c\u5168\u4f53\u306e\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u3068\u52b9\u7387\u5316\u3092\u4fc3\u9032\u3059\u308b\u672a\u6765\u3092\u6307\u3057\u793a\u3057\u3066\u3044\u308b\u3002<\/p>","protected":false},"excerpt":{"rendered":"<p>Few-shot learning has emerged as a crucial area of research in machine learning, aiming to develop algorithms that can learn from limited labeled examples. This capability is essential for many real-world applications where data is scarce, expensive, or time-consuming to obtain. We will explore five seminal research papers that have significantly advanced the field of few-shot learning by being implemented. These papers introduce novel approaches, architectures, and evaluation protocols, pushing the boundaries of what is possible in this challenging domain. By examining these contributions, we hope to provide a comprehensive overview of the current state of few-shot learning and inspire further research in this exciting area. 1. Matching Networks for [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":13005,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[100,67,134],"tags":[],"class_list":["post-12977","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai","category-ml-nlp","category-research-stats"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top 5 Research Papers on Few-Shot Learning - Skim AI<\/title>\n<meta name=\"description\" content=\"Explore five groundbreaking research papers that have significantly advanced few-shot learning, offering novel approaches and architectures to tackle learning from limited data. 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