{"id":3714,"date":"2026-01-30T14:33:06","date_gmt":"2026-01-30T14:33:06","guid":{"rendered":"https:\/\/umaax.com\/shenmeshilingyangbenxuexizeroshotlearningzslaibaikezhishi\/"},"modified":"2026-01-30T14:33:06","modified_gmt":"2026-01-30T14:33:06","slug":"shenmeshilingyangbenxuexizeroshotlearningzslaibaikezhishi","status":"publish","type":"post","link":"https:\/\/umaax.com\/en\/shenmeshilingyangbenxuexizeroshotlearningzslaibaikezhishi\/","title":{"rendered":"\u4ec0\u4e48\u662f\u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09- AI\u767e\u79d1\u77e5\u8bc6"},"content":{"rendered":"<p>\u96f6\u6837\u672c\u5b66\u4e60\u4f5c\u4e3a\u4e00\u79cd\u524d\u6cbf\u7684\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u5df2\u7ecf\u5728\u7406\u8bba\u548c\u5e94\u7528\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u8fdb\u5c55\u3002\u901a\u8fc7\u5229\u7528\u8f85\u52a9\u4fe1\u606f\u3001\u8fc1\u79fb\u5b66\u4e60\u3001\u5c5e\u6027\u548c\u5d4c\u5165\u65b9\u6cd5\uff0c\u4ee5\u53ca\u751f\u6210\u6a21\u578b\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u80fd\u591f\u5904\u7406\u4f20\u7edf\u76d1\u7763\u5b66\u4e60\u96be\u4ee5\u89e3\u51b3\u7684\u95ee\u9898\u3002\u5c3d\u7ba1\u5b58\u5728\u6311\u6218\uff0c\u5982\u5e7f\u4e49\u96f6\u6837\u672c\u5b66\u4e60\u3001\u67a2\u7ebd\u5316\u95ee\u9898\u548c\u6620\u5c04\u57df\u504f\u79fb\u95ee\u9898\uff0c\u4f46\u7814\u7a76\u8005\u4eec\u5df2\u7ecf\u63d0\u51fa\u4e86\u591a\u79cd\u89e3\u51b3\u65b9\u6848\uff0c\u5e76\u5728\u4e0d\u65ad\u63a2\u7d22\u65b0\u7684\u65b9\u6cd5\u548c\u5e94\u7528\u9886\u57df\u3002\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u6709\u671b\u5728\u672a\u6765\u53d1\u6325\u66f4\u5927\u7684\u4f5c\u7528\u3002<\/p>\n<h2>\u4ec0\u4e48\u662f\u96f6\u6837\u672c\u5b66\u4e60<\/h2>\n<p>\u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u573a\u666f\uff0c\u5176\u4e2dAI\u6a21\u578b\u88ab\u8bad\u7ec3\u4ee5\u8bc6\u522b\u548c\u5206\u7c7b\u5bf9\u8c61\u6216\u6982\u5ff5\uff0c\u4e8b\u5148\u4e0d\u77e5\u9053\u8fd9\u4e9b\u7c7b\u522b\u6216\u6982\u5ff5\u7684\u4efb\u4f55\u793a\u4f8b\u3002\u5927\u591a\u6570\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u901a\u8fc7\u76d1\u7763\u5b66\u4e60\u8fdb\u884c\u8bad\u7ec3\uff0c\u9700\u8981\u5927\u91cf\u76f8\u5173\u6570\u636e\u7c7b\u7684\u6807\u6ce8\u793a\u4f8b\u3002\u7136\u800c\uff0c\u5728\u67d0\u4e9b\u73b0\u5b9e\u573a\u666f\u4e2d\uff0c\u5bf9\u5927\u91cf\u6570\u636e\u6837\u672c\u8fdb\u884c\u6ce8\u91ca\u65e2\u6602\u8d35\u53c8\u8017\u65f6\uff0c\u5728\u7f55\u89c1\u75be\u75c5\u548c\u65b0\u53d1\u73b0\u7269\u79cd\u7b49\u9886\u57df\uff0c\u53ef\u80fd\u5f88\u5c11\u6216\u6839\u672c\u6ca1\u6709\u4efb\u4f55\u5148\u4f8b\u3002\u96f6\u6837\u672c\u5b66\u4e60\u7684\u76ee\u6807\u662f\u4f7f\u6a21\u578b\u80fd\u4ee5\u6700\u4f4e\u8bad\u7ec3\u5f00\u9500\u5feb\u901f\u63a8\u5e7f\u5230\u5927\u91cf\u8bed\u4e49\u7c7b\u522b\u3002<\/p>\n<h2>\u96f6\u6837\u672c\u5b66\u4e60\u7684\u5de5\u4f5c\u539f\u7406<\/h2>\n<p>\u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09\u7684\u6838\u5fc3\u601d\u60f3\u662f\u57fa\u4e8e\u8f85\u52a9\u4fe1\u606f\uff08\u5982\u7c7b\u522b\u7684\u8bed\u4e49\u63cf\u8ff0\u6216\u5c5e\u6027\u4fe1\u606f\uff09\u6765\u5b9e\u73b0\u5bf9\u65b0\u7c7b\u522b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u5728\u8bad\u7ec3\u9636\u6bb5\uff0c\u6a21\u578b\u901a\u8fc7\u5b66\u4e60\u5df2\u77e5\u7c7b\u522b\u7684\u7279\u5f81\u8868\u793a\u548c\u8f85\u52a9\u4fe1\u606f\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5728\u63a8\u7406\u9636\u6bb5\u80fd\u591f\u8bc6\u522b\u672a\u89c1\u8fc7\u7684\u65b0\u7c7b\u522b\u3002\u96f6\u6837\u672c\u5b66\u4e60\u95ee\u9898\u4f1a\u5229\u7528\u8f85\u52a9\u4fe1\u606f\uff0c\u5982\u6587\u672c\u63cf\u8ff0\u3001\u5c5e\u6027\u3001\u5d4c\u5165\u5f0f\u8868\u793a\u6216\u4e0e\u5f53\u524d\u4efb\u52a1\u76f8\u5173\u7684\u5176\u4ed6\u8bed\u4e49\u4fe1\u606f\u3002\u8fd9\u4e9b\u4fe1\u606f\u5145\u5f53\u4e86\u5df2\u77e5\u7c7b\u548c\u672a\u77e5\u7c7b\u4e4b\u95f4\u7684\u6865\u6881\u3002ZSL\u5e38\u4f1a\u57fa\u4e8e\u8fc1\u79fb\u5b66\u4e60\uff0c\u5373\u91cd\u65b0\u5229\u7528\u9884\u8bad\u7ec3\u7684\u6a21\u578b\u8fdb\u884c\u65b0\u4efb\u52a1\uff0c\u4e0d\u662f\u4ece\u5934\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u9884\u8bad\u7ec3\u7684BERT\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u96f6\u6837\u672c\u6587\u672c\u5206\u7c7b\uff0c\u9884\u8bad\u7ec3\u7684CNN\u5982ResNet\u53ef\u4ee5\u7528\u4e8e\u96f6\u6837\u672c\u56fe\u50cf\u5206\u7c7b\u3002<\/p>\n<h2>\u96f6\u6837\u672c\u5b66\u4e60\u7684\u4e3b\u8981\u5e94\u7528<\/h2>\n<p>\u96f6\u6837\u672c\u5b66\u4e60\u5728\u591a\u4e2a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<ul>\n<li><strong>\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1<\/strong>\uff1a\u5728\u56fe\u50cf\u5206\u7c7b\u3001\u8bed\u4e49\u5206\u5272\u3001\u76ee\u6807\u68c0\u6d4b\u7b49\u4efb\u52a1\u4e2d\uff0c\u6a21\u578b\u901a\u8fc7\u5b66\u4e60\u5df2\u77e5\u7c7b\u522b\u7684\u8868\u793a\uff0c\u57fa\u4e8e\u7c7b\u522b\u4e4b\u95f4\u7684\u8bed\u4e49\u76f8\u4f3c\u6027\uff0c\u5bf9\u65b0\u7c7b\u522b\u8fdb\u884c\u63a8\u7406\u548c\u5206\u7c7b\u3002<\/li>\n<li><strong>\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1<\/strong>\uff1a\u5728NLP\u9886\u57df\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u7684\u5173\u952e\u6280\u672f\u662f\u5c06\u7c7b\u522b\u6807\u7b7e\u548c\u6587\u672c\u6620\u5c04\u5230\u540c\u4e00\u8bed\u4e49\u7a7a\u95f4\u4e2d\uff0c\u5b9e\u73b0\u5bf9\u5355\u4e2a\u793a\u4f8b\u7684\u5206\u7c7b\u800c\u65e0\u9700\u4efb\u4f55\u6807\u6ce8\u6570\u636e\u3002<\/li>\n<li><strong>\u751f\u6210\u5f0f\u5efa\u6a21<\/strong>\uff1a\u96f6\u6837\u672c\u5b66\u4e60\u4f7f\u751f\u6210\u6a21\u578b\u80fd\u751f\u6210\u8bad\u7ec3\u6570\u636e\u4e2d\u5f88\u5c11\u6216\u6ca1\u6709\u51fa\u73b0\u8fc7\u7684\u65b0\u6837\u672c\uff0c\u4f8b\u5982\uff0c\u8bad\u7ec3\u4e8e\u52a8\u7269\u56fe\u50cf\u7684\u751f\u6210\u6a21\u578b\uff0c\u80fd\u901a\u8fc7\u96f6\u6837\u672c\u5b66\u4e60\u751f\u6210\u7f55\u89c1\u52a8\u7269\u7684\u56fe\u50cf\u3002<\/li>\n<li><strong>\u5927\u578b\u8bed\u8a00\u6a21\u578b<\/strong>\uff1a\u4e00\u4e9b\u5927\u578b\u8bed\u8a00\u6a21\u578b\u5982GPT-1\u5df2\u5c55\u73b0\u51fa\u901a\u8fc7\u63d0\u793a\u5c31\u80fd\u6267\u884c\u5404\u79cd\u4efb\u52a1\u7684\u80fd\u529b\uff0c\u65e0\u9700\u663e\u5f0f\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n<h2>\u96f6\u6837\u672c\u5b66\u4e60\u9762\u4e34\u7684\u6311\u6218<\/h2>\n<p>\u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09\u4f5c\u4e3a\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u5176\u76ee\u6807\u662f\u5728\u6ca1\u6709\u76f4\u63a5\u6837\u672c\u7684\u60c5\u51b5\u4e0b\u5bf9\u65b0\u7c7b\u522b\u8fdb\u884c\u8bc6\u522b\u548c\u5206\u7c7b\u3002\u5c3d\u7ba1\u8fd9\u4e00\u9886\u57df\u5df2\u7ecf\u53d6\u5f97\u4e86\u663e\u8457\u7684\u8fdb\u5c55\uff0c\u4f46\u5728\u672a\u6765\u7684\u53d1\u5c55\u4e2d\u4ecd\u7136\u9762\u4e34\u7740\u4e00\u7cfb\u5217\u7684\u6280\u672f\u74f6\u9888\u3001\u5e94\u7528\u56f0\u96be\u4ee5\u53ca\u5b9e\u9645\u843d\u5730\u65f6\u53ef\u80fd\u9047\u5230\u7684\u969c\u788d\uff1a<\/p>\n<ul>\n<li><strong>\u8bed\u4e49\u9e3f\u6c9f\uff08Semantic Gap\uff09<\/strong> \u8bed\u4e49\u9e3f\u6c9f\u6307\u7684\u662f\u89c6\u89c9\u7279\u5f81\u4e0e\u8bed\u4e49\u63cf\u8ff0\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u5728\u96f6\u6837\u672c\u5b66\u4e60\u4e2d\uff0c\u6a21\u578b\u9700\u8981\u5c06\u89c6\u89c9\u7279\u5f81\u6620\u5c04\u5230\u8bed\u4e49\u7a7a\u95f4\uff0c\u4f46\u7531\u4e8e\u4e24\u8005\u4e4b\u95f4\u7684\u672c\u8d28\u5dee\u5f02\uff0c\u8fd9\u4e00\u6620\u5c04\u8fc7\u7a0b\u975e\u5e38\u590d\u6742\u3002<\/li>\n<li><strong>\u6570\u636e\u7a00\u7f3a\uff08Data Scarcity\uff09<\/strong> \u96f6\u6837\u672c\u5b66\u4e60\u7684\u6838\u5fc3\u95ee\u9898\u4e4b\u4e00\u662f\u5982\u4f55\u5904\u7406\u6570\u636e\u7a00\u7f3a\u7684\u60c5\u51b5\u3002\u7531\u4e8e\u65b0\u7c7b\u522b\u6ca1\u6709\u6807\u6ce8\u6837\u672c\uff0c\u6a21\u578b\u65e0\u6cd5\u76f4\u63a5\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u65b0\u7c7b\u522b\u7684\u7279\u5f81\u3002\u8981\u6c42\u6a21\u578b\u80fd\u591f\u5229\u7528\u6709\u9650\u7684\u8f85\u52a9\u4fe1\u606f\uff08\u5982\u7c7b\u522b\u63cf\u8ff0\u3001\u5c5e\u6027\u7b49\uff09\u6765\u6cdb\u5316\u5230\u65b0\u7c7b\u522b\uff0c\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u662f\u4e00\u4e2a\u5de8\u5927\u7684\u6311\u6218\u3002<\/li>\n<li><strong>\u7c7b\u95f4\u76f8\u4f3c\u6027\uff08Inter-class Similarity\uff09<\/strong> \u5728\u96f6\u6837\u672c\u5b66\u4e60\u4e2d\uff0c\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u96be\u4ee5\u533a\u5206\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u6210\u672c\uff08Computational Cost\uff09<\/strong> \u96f6\u6837\u672c\u5b66\u4e60\u6a21\u578b\u9700\u8981\u590d\u6742\u7684\u6620\u5c04\u51fd\u6570\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8f83\u9ad8\u7684\u8ba1\u7b97\u6210\u672c\u3002\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u65f6\uff0c\u8ba1\u7b97\u8d44\u6e90\u7684\u9700\u6c42\u53ef\u80fd\u4f1a\u6210\u4e3a\u4e00\u4e2a\u9650\u5236\u56e0\u7d20\u3002<\/li>\n<li><strong>\u6cdb\u5316\u80fd\u529b\uff08Generalization\uff09<\/strong> \u96f6\u6837\u672c\u5b66\u4e60\u6a21\u578b\u9700\u8981\u5177\u5907\u5f3a\u5927\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4ee5\u4fbf\u5728\u6ca1\u6709\u76f4\u63a5\u6837\u672c\u7684\u60c5\u51b5\u4e0b\u5bf9\u65b0\u7c7b\u522b\u8fdb\u884c\u51c6\u786e\u5206\u7c7b\u3002\u7531\u4e8e\u7f3a\u4e4f\u8db3\u591f\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u6a21\u578b\u53ef\u80fd\u4f1a\u8fc7\u62df\u5408\u4e8e\u5df2\u77e5\u7c7b\u522b\u7684\u7279\u5f81\uff0c\u5bfc\u81f4\u5bf9\u65b0\u7c7b\u522b\u7684\u6cdb\u5316\u80fd\u529b\u4e0d\u8db3\u3002<\/li>\n<li><strong>\u591a\u6a21\u6001\u5b66\u4e60\uff08Multimodal Learning\uff09<\/strong> \u5728\u5904\u7406\u591a\u6a21\u6001\u6570\u636e\u65f6\uff0c\u5982\u56fe\u50cf\u548c\u6587\u672c\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u9700\u8981\u6709\u6548\u5730\u878d\u5408\u4e0d\u540c\u6a21\u6001\u7684\u4fe1\u606f\u3002<\/li>\n<li><strong>\u751f\u6210\u5f0f\u6a21\u578b\uff08Generative Models\uff09<\/strong> \u751f\u6210\u5f0f\u6a21\u578b\u5728\u96f6\u6837\u672c\u5b66\u4e60\u4e2d\u7528\u4e8e\u751f\u6210\u65b0\u7c7b\u522b\u7684\u6570\u636e\u6837\u672c\uff0c\u5f25\u8865\u6570\u636e\u7a00\u7f3a\u7684\u95ee\u9898\u3002<\/li>\n<li><strong>\u6570\u636e\u96c6\u504f\u5dee\uff08Dataset Bias\uff09<\/strong> \u73b0\u6709\u7684\u96f6\u6837\u672c\u5b66\u4e60\u6570\u636e\u96c6\u53ef\u80fd\u5b58\u5728\u504f\u5dee\uff0c\u4f8b\u5982\uff0c\u67d0\u4e9b\u7c7b\u522b\u53ef\u80fd\u5728\u8bad\u7ec3\u96c6\u4e2d\u8fc7\u5ea6\u8868\u793a\uff0c\u5728\u6d4b\u8bd5\u96c6\u4e2d\u5219\u4e0d\u5e38\u89c1\u3002\u8fd9\u79cd\u504f\u5dee\u4f1a\u5f71\u54cd\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u548c\u5b9e\u9645\u5e94\u7528\u6548\u679c\u3002<\/li>\n<li><strong>\u6a21\u578b\u53ef\u89e3\u91ca\u6027\uff08Model Interpretability\uff09<\/strong> \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u662f\u4e00\u4e2a\u91cd\u8981\u56e0\u7d20\u3002\u96f6\u6837\u672c\u5b66\u4e60\u6a21\u578b\u8f83\u4e3a\u590d\u6742\uff0c\u96be\u4ee5\u89e3\u91ca\u5176\u51b3\u7b56\u8fc7\u7a0b\uff0c\u4f1a\u9650\u5236\u5728\u67d0\u4e9b\u9886\u57df\u7684\u5e94\u7528\u3002<\/li>\n<li><strong>\u5b9e\u65f6\u6027\u80fd\uff08Real-time Performance\uff09<\/strong> \u5728\u9700\u8981\u5b9e\u65f6\u54cd\u5e94\u7684\u5e94\u7528\u573a\u666f\u4e2d\uff0c\u5982\u81ea\u52a8\u9a7e\u9a76\u6216\u5b89\u5168\u76d1\u63a7\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u6a21\u578b\u9700\u8981\u5728\u6781\u77ed\u7684\u65f6\u95f4\u5185\u505a\u51fa\u51c6\u786e\u7684\u9884\u6d4b\u3002<\/li>\n<\/ul>\n<h2>\u96f6\u6837\u672c\u5b66\u4e60\u7684\u53d1\u5c55\u524d\u666f<\/h2>\n<p>\u5c3d\u7ba1\u96f6\u6837\u672c\u5b66\u4e60\u5df2\u7ecf\u53d6\u5f97\u4e86\u663e\u8457\u7684\u8fdb\u5c55\uff0c\u4f46\u4ecd\u6709\u8bb8\u591a\u6311\u6218\u548c\u672a\u6765\u7684\u7814\u7a76\u65b9\u5411\u3002\u4f8b\u5982\uff0c\u5982\u4f55\u5904\u7406\u7c7b\u522b\u4e0d\u5e73\u8861\u95ee\u9898\u3001\u5982\u4f55\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3001\u5982\u4f55\u89e3\u51b3\u67a2\u7ebd\u5316\u95ee\u9898\u548c\u6620\u5c04\u57df\u504f\u79fb\u95ee\u9898\u7b49\u3002\u6b64\u5916\uff0c\u7814\u7a76\u8005\u4eec\u4e5f\u5728\u63a2\u7d22\u65b0\u7684\u5e94\u7528\u9886\u57df\uff0c\u5982\u9065\u611f\u56fe\u50cf\u8bc6\u522b\u3001\u7ec6\u7c92\u5ea6\u7269\u4f53\u8bc6\u522b\u7b49\u3002\u672a\u6765\u7684\u7814\u7a76\u9700\u8981\u5173\u6ce8\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3001\u89e3\u51b3\u6570\u636e\u7a00\u7f3a\u95ee\u9898\u3001\u4f18\u5316\u8ba1\u7b97\u6548\u7387\u3001\u589e\u5f3a\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u7b49\u65b9\u9762\u3002\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u4e0d\u65ad\u53d1\u5c55\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u6709\u671b\u5728\u672a\u6765\u53d1\u6325\u66f4\u5927\u7684\u4f5c\u7528\u3002<\/p>","protected":false},"excerpt":{"rendered":"<p>\u96f6\u6837\u672c\u5b66\u4e60\u4f5c\u4e3a\u4e00\u79cd\u524d\u6cbf\u7684\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u5df2\u7ecf\u5728\u7406\u8bba\u548c\u5e94\u7528\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u8fdb\u5c55\u3002\u901a\u8fc7\u5229\u7528\u8f85\u52a9\u4fe1\u606f\u3001\u8fc1\u79fb\u5b66\u4e60\u3001\u5c5e\u6027\u548c\u5d4c\u5165\u65b9\u6cd5\uff0c\u4ee5\u53ca\u751f\u6210\u6a21\u578b\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u80fd\u591f\u5904\u7406\u4f20\u7edf\u76d1\u7763\u5b66\u4e60\u96be\u4ee5\u89e3\u51b3\u7684\u95ee\u9898\u3002\u5c3d\u7ba1\u5b58\u5728\u6311\u6218\uff0c\u5982\u5e7f\u4e49\u96f6\u6837\u672c\u5b66\u4e60\u3001\u67a2\u7ebd\u5316\u95ee\u9898\u548c\u6620\u5c04\u57df\u504f\u79fb\u95ee\u9898\uff0c\u4f46\u7814\u7a76\u8005\u4eec\u5df2\u7ecf\u63d0\u51fa\u4e86\u591a\u79cd\u89e3\u51b3\u65b9\u6848\uff0c\u5e76\u5728\u4e0d\u65ad\u63a2\u7d22\u65b0\u7684\u65b9\u6cd5\u548c\u5e94\u7528\u9886\u57df\u3002\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u6709\u671b\u5728\u672a\u6765\u53d1\u6325\u66f4\u5927\u7684\u4f5c\u7528\u3002 \u4ec0\u4e48\u662f\u96f6\u6837\u672c\u5b66\u4e60 \u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u573a\u666f\uff0c\u5176\u4e2dAI\u6a21\u578b\u88ab\u8bad\u7ec3\u4ee5\u8bc6\u522b\u548c\u5206\u7c7b\u5bf9\u8c61\u6216\u6982\u5ff5\uff0c\u4e8b\u5148\u4e0d\u77e5\u9053\u8fd9\u4e9b\u7c7b\u522b\u6216\u6982\u5ff5\u7684\u4efb\u4f55\u793a\u4f8b\u3002\u5927\u591a\u6570\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u901a\u8fc7\u76d1\u7763\u5b66\u4e60\u8fdb\u884c\u8bad\u7ec3\uff0c\u9700\u8981\u5927\u91cf\u76f8\u5173\u6570\u636e\u7c7b\u7684\u6807\u6ce8\u793a\u4f8b\u3002\u7136\u800c\uff0c\u5728\u67d0\u4e9b\u73b0\u5b9e\u573a\u666f\u4e2d\uff0c\u5bf9\u5927\u91cf\u6570\u636e\u6837\u672c\u8fdb\u884c\u6ce8\u91ca\u65e2\u6602\u8d35\u53c8\u8017\u65f6\uff0c\u5728\u7f55\u89c1\u75be\u75c5\u548c\u65b0\u53d1\u73b0\u7269\u79cd\u7b49\u9886\u57df\uff0c\u53ef\u80fd\u5f88\u5c11\u6216\u6839\u672c\u6ca1\u6709\u4efb\u4f55\u5148\u4f8b\u3002\u96f6\u6837\u672c\u5b66\u4e60\u7684\u76ee\u6807\u662f\u4f7f\u6a21\u578b\u80fd\u4ee5\u6700\u4f4e\u8bad\u7ec3\u5f00\u9500\u5feb\u901f\u63a8\u5e7f\u5230\u5927\u91cf\u8bed\u4e49\u7c7b\u522b\u3002 \u96f6\u6837\u672c\u5b66\u4e60\u7684\u5de5\u4f5c\u539f\u7406 \u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09\u7684\u6838\u5fc3\u601d\u60f3\u662f\u57fa\u4e8e\u8f85\u52a9\u4fe1\u606f\uff08\u5982\u7c7b\u522b\u7684\u8bed\u4e49\u63cf\u8ff0\u6216\u5c5e\u6027\u4fe1\u606f\uff09\u6765\u5b9e\u73b0\u5bf9\u65b0\u7c7b\u522b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u5728\u8bad\u7ec3\u9636\u6bb5\uff0c\u6a21\u578b\u901a\u8fc7\u5b66\u4e60\u5df2\u77e5\u7c7b\u522b\u7684\u7279\u5f81\u8868\u793a\u548c\u8f85\u52a9\u4fe1\u606f\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5728\u63a8\u7406\u9636\u6bb5\u80fd\u591f\u8bc6\u522b\u672a\u89c1\u8fc7\u7684\u65b0\u7c7b\u522b\u3002\u96f6\u6837\u672c\u5b66\u4e60\u95ee\u9898\u4f1a\u5229\u7528\u8f85\u52a9\u4fe1\u606f\uff0c\u5982\u6587\u672c\u63cf\u8ff0\u3001\u5c5e\u6027\u3001\u5d4c\u5165\u5f0f\u8868\u793a\u6216\u4e0e\u5f53\u524d\u4efb\u52a1\u76f8\u5173\u7684\u5176\u4ed6\u8bed\u4e49\u4fe1\u606f\u3002\u8fd9\u4e9b\u4fe1\u606f\u5145\u5f53\u4e86\u5df2\u77e5\u7c7b\u548c\u672a\u77e5\u7c7b\u4e4b\u95f4\u7684\u6865\u6881\u3002ZSL\u5e38\u4f1a\u57fa\u4e8e\u8fc1\u79fb\u5b66\u4e60\uff0c\u5373\u91cd\u65b0\u5229\u7528\u9884\u8bad\u7ec3\u7684\u6a21\u578b\u8fdb\u884c\u65b0\u4efb\u52a1\uff0c\u4e0d\u662f\u4ece\u5934\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u9884\u8bad\u7ec3\u7684BERT\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u96f6\u6837\u672c\u6587\u672c\u5206\u7c7b\uff0c\u9884\u8bad\u7ec3\u7684CNN\u5982ResNet\u53ef\u4ee5\u7528\u4e8e\u96f6\u6837\u672c\u56fe\u50cf\u5206\u7c7b\u3002 \u96f6\u6837\u672c\u5b66\u4e60\u7684\u4e3b\u8981\u5e94\u7528 \u96f6\u6837\u672c\u5b66\u4e60\u5728\u591a\u4e2a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u573a\u666f\uff1a \u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\uff1a\u5728\u56fe\u50cf\u5206\u7c7b\u3001\u8bed\u4e49\u5206\u5272\u3001\u76ee\u6807\u68c0\u6d4b\u7b49\u4efb\u52a1\u4e2d\uff0c\u6a21\u578b\u901a\u8fc7\u5b66\u4e60\u5df2\u77e5\u7c7b\u522b\u7684\u8868\u793a\uff0c\u57fa\u4e8e\u7c7b\u522b\u4e4b\u95f4\u7684\u8bed\u4e49\u76f8\u4f3c\u6027\uff0c\u5bf9\u65b0\u7c7b\u522b\u8fdb\u884c\u63a8\u7406\u548c\u5206\u7c7b\u3002 \u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff1a\u5728NLP\u9886\u57df\uff0c\u96f6\u6837\u672c\u5b66\u4e60\u7684\u5173\u952e\u6280\u672f\u662f\u5c06\u7c7b\u522b\u6807\u7b7e\u548c\u6587\u672c\u6620\u5c04\u5230\u540c\u4e00\u8bed\u4e49\u7a7a\u95f4\u4e2d\uff0c\u5b9e\u73b0\u5bf9\u5355\u4e2a\u793a\u4f8b\u7684\u5206\u7c7b\u800c\u65e0\u9700\u4efb\u4f55\u6807\u6ce8\u6570\u636e\u3002 \u751f\u6210\u5f0f\u5efa\u6a21\uff1a\u96f6\u6837\u672c\u5b66\u4e60\u4f7f\u751f\u6210\u6a21\u578b\u80fd\u751f\u6210\u8bad\u7ec3\u6570\u636e\u4e2d\u5f88\u5c11\u6216\u6ca1\u6709\u51fa\u73b0\u8fc7\u7684\u65b0\u6837\u672c\uff0c\u4f8b\u5982\uff0c\u8bad\u7ec3\u4e8e\u52a8\u7269\u56fe\u50cf\u7684\u751f\u6210\u6a21\u578b\uff0c\u80fd\u901a\u8fc7\u96f6\u6837\u672c\u5b66\u4e60\u751f\u6210\u7f55\u89c1\u52a8\u7269\u7684\u56fe\u50cf\u3002 \u5927\u578b\u8bed\u8a00\u6a21\u578b\uff1a\u4e00\u4e9b\u5927\u578b\u8bed\u8a00\u6a21\u578b\u5982GPT-1\u5df2\u5c55\u73b0\u51fa\u901a\u8fc7\u63d0\u793a\u5c31\u80fd\u6267\u884c\u5404\u79cd\u4efb\u52a1\u7684\u80fd\u529b\uff0c\u65e0\u9700\u663e\u5f0f\u8bad\u7ec3\u3002 \u96f6\u6837\u672c\u5b66\u4e60\u9762\u4e34\u7684\u6311\u6218 \u96f6\u6837\u672c\u5b66\u4e60\uff08Zero-Shot Learning, ZSL\uff09\u4f5c\u4e3a\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u5176\u76ee\u6807\u662f\u5728\u6ca1\u6709\u76f4\u63a5\u6837\u672c\u7684\u60c5\u51b5\u4e0b\u5bf9\u65b0\u7c7b\u522b\u8fdb\u884c\u8bc6\u522b\u548c\u5206\u7c7b\u3002\u5c3d\u7ba1\u8fd9\u4e00\u9886\u57df\u5df2\u7ecf\u53d6\u5f97\u4e86\u663e\u8457\u7684\u8fdb\u5c55\uff0c\u4f46\u5728\u672a\u6765\u7684\u53d1\u5c55\u4e2d\u4ecd\u7136\u9762\u4e34\u7740\u4e00\u7cfb\u5217\u7684\u6280\u672f\u74f6\u9888\u3001\u5e94\u7528\u56f0\u96be\u4ee5\u53ca\u5b9e\u9645\u843d\u5730\u65f6\u53ef\u80fd\u9047\u5230\u7684\u969c\u788d\uff1a \u8bed\u4e49\u9e3f\u6c9f\uff08Semantic Gap\uff09 \u8bed\u4e49\u9e3f\u6c9f\u6307\u7684\u662f\u89c6\u89c9\u7279\u5f81\u4e0e\u8bed\u4e49\u63cf\u8ff0\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u5728\u96f6\u6837\u672c\u5b66\u4e60\u4e2d\uff0c\u6a21\u578b\u9700\u8981\u5c06\u89c6\u89c9\u7279\u5f81\u6620\u5c04\u5230\u8bed\u4e49\u7a7a\u95f4\uff0c\u4f46\u7531\u4e8e\u4e24\u8005\u4e4b\u95f4\u7684\u672c\u8d28\u5dee\u5f02\uff0c\u8fd9\u4e00\u6620\u5c04\u8fc7\u7a0b\u975e\u5e38\u590d\u6742\u3002 \u6570\u636e\u7a00\u7f3a\uff08Data Scarcity\uff09 \u96f6\u6837\u672c\u5b66\u4e60\u7684\u6838\u5fc3\u95ee\u9898\u4e4b\u4e00\u662f\u5982\u4f55\u5904\u7406\u6570\u636e\u7a00\u7f3a\u7684\u60c5\u51b5\u3002\u7531\u4e8e\u65b0\u7c7b\u522b\u6ca1\u6709\u6807\u6ce8\u6837\u672c\uff0c\u6a21\u578b\u65e0\u6cd5\u76f4\u63a5\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u65b0\u7c7b\u522b\u7684\u7279\u5f81\u3002\u8981\u6c42\u6a21\u578b\u80fd\u591f\u5229\u7528\u6709\u9650\u7684\u8f85\u52a9\u4fe1\u606f\uff08\u5982\u7c7b\u522b\u63cf\u8ff0\u3001\u5c5e\u6027\u7b49\uff09\u6765\u6cdb\u5316\u5230\u65b0\u7c7b\u522b\uff0c\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u662f\u4e00\u4e2a\u5de8\u5927\u7684\u6311\u6218\u3002 \u7c7b\u95f4\u76f8\u4f3c\u6027\uff08Inter-class Similarity\uff09 \u5728\u96f6\u6837\u672c\u5b66\u4e60\u4e2d\uff0c\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u96be\u4ee5\u533a\u5206\u3002 \u8ba1\u7b97\u6210\u672c\uff08Computational Cost\uff09 \u96f6\u6837\u672c\u5b66\u4e60\u6a21\u578b\u9700\u8981\u590d\u6742\u7684\u6620\u5c04\u51fd\u6570\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8f83\u9ad8\u7684\u8ba1\u7b97\u6210\u672c\u3002\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u65f6\uff0c\u8ba1\u7b97\u8d44\u6e90\u7684\u9700\u6c42\u53ef\u80fd\u4f1a\u6210\u4e3a\u4e00\u4e2a\u9650\u5236\u56e0\u7d20\u3002 \u6cdb\u5316\u80fd\u529b\uff08Generalization\uff09 \u96f6\u6837\u672c\u5b66\u4e60\u6a21\u578b\u9700\u8981\u5177\u5907\u5f3a\u5927\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4ee5\u4fbf\u5728\u6ca1\u6709\u76f4\u63a5\u6837\u672c\u7684\u60c5\u51b5\u4e0b\u5bf9\u65b0\u7c7b\u522b\u8fdb\u884c\u51c6\u786e\u5206\u7c7b\u3002\u7531\u4e8e\u7f3a\u4e4f\u8db3\u591f\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u6a21\u578b\u53ef\u80fd\u4f1a\u8fc7\u62df\u5408\u4e8e\u5df2\u77e5\u7c7b\u522b\u7684\u7279\u5f81\uff0c\u5bfc\u81f4\u5bf9\u65b0\u7c7b\u522b\u7684\u6cdb\u5316\u80fd\u529b\u4e0d\u8db3\u3002 \u591a\u6a21\u6001\u5b66\u4e60\uff08Multimodal Learning\uff09 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