{"id":32812,"date":"2022-03-01T16:26:02","date_gmt":"2022-03-01T08:26:02","guid":{"rendered":"https:\/\/swarma.org\/?p=32812"},"modified":"2022-03-01T16:26:02","modified_gmt":"2022-03-01T08:26:02","slug":"%e5%89%8d%e6%b2%bf%e7%ae%97%e6%b3%95%ef%bc%9a%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8%e7%be%a4%e8%ae%ba%e8%bf%9b%e8%a1%8c%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e4%b8%8b%e7%9a%84%e5%9b%a0%e6%9e%9c%e7%89%b9","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=32812","title":{"rendered":"\u524d\u6cbf\u7b97\u6cd5\uff1a\u5982\u4f55\u5229\u7528\u7fa4\u8bba\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u4e0b\u7684\u56e0\u679c\u7279\u5f81\u89e3\u8026"},"content":{"rendered":"
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\u7814\u7a76\u9886\u57df\uff1a\u56e0\u679c\u5173\u7cfb\uff0c\u7fa4\u8bba\uff0c\u7b97\u6cd5\uff0c\u6df1\u5ea6\u5b66\u4e60<\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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\u56fe1. \u89e3\u8026\u56e0\u679c\u673a\u5236[1]<\/span><\/section>\n

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\u56fe2. \u8bed\u4e49\u56e0\u5b50U\u4e0e\u7279\u5f81X\u4e00\u4e00\u5bf9\u5e94[1]<\/span><\/section>\n

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\u7136\u540e\u6211\u4eec\u5728\u56fe1\u7684\u57fa\u7840\u4e0a\u5411\u4e0b\u753b\u4e9b\u7bad\u5934\uff0c\u4ee3\u8868\u4e86\u5f97\u5230\u7279\u5f81\u7684\u6b65\u9aa4\u3002\u6211\u4eec\u5e0c\u671b\u5b66\u4e60\u5230\u8fd9\u6837\u7684\u4e00\u4e2a\u7279\u5f81\u62bd\u53d6\u5668\uff0c\u53ef\u4ee5\u4ece\u4e00\u4e2a\u7ed9\u5b9a\u7684\u89c2\u6d4b\u56fe\u7247\u5f97\u5230\u7279\u5f81\u7a7a\u95f4\u4e2d\u7684X\u3002X\u53ef\u4ee5\u88ab\u5206\u89e3\u4e3aX1\u5230Xm\uff0c\u5e76\u6070\u597d\u5206\u522b\u5bf9\u5e94\u4e0a\u9762\u7684\u8bed\u4e49U1\u5230UM\u3002\u5982\u679c\u7279\u5f81X1\u5230Xm\u662f\u89e3\u8026\u7684\uff0c\u90a3\u4e48\u5f53\u6211\u4eec\u5e72\u9884\u67d0\u4e00\u4e2a\u7279\u5b9a\u7684\u8bed\u4e49Ui\u65f6\uff0c\u4ec5\u4ec5\u4f1a\u5f71\u54cd\u5230\u5bf9\u5e94\u7684\u7279\u5f81\u7684Xi\uff0c\u800c\u4e0d\u4f1a\u5f71\u54cd\u5230\u5176\u4ed6\u7684\u90e8\u5206\u3002<\/span><\/section>\n

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\u56fe3. \u5206\u5e03\u5916\u6cdb\u5316\u6570\u636e\u96c6\uff08Out-of-Distribution Generalization\uff09\u6837\u4f8b<\/span><\/section>\n

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\u56fe4. Zero-Shot Learning\u6570\u636e\u96c6\u6837\u4f8b<\/span><\/section>\n

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\u7b2c\u4e8c\u4e2a\u597d\u5904\u4f53\u73b0\u5728Zero-Shot Learning\u4e2d\u3002\u5728\u8fd9\u4e2a\u4efb\u52a1\u91cc\uff0c\u6d4b\u8bd5\u96c6\u4e2d\u4f1a\u51fa\u73b0\u8bad\u7ec3\u96c6\u4e2d\u6ca1\u89c1\u8fc7\u7684\u65b0\u79cd\u7c7b\u3002\u4f46\u662f\u5982\u679c\u80fd\u505a\u5230\u7279\u5f81\u89e3\u8026\uff0c\u65b0\u7684\u79cd\u7c7b\u4e5f\u80fd\u7528\u5df2\u5b66\u8fc7\u7684\u7279\u5f81\u8fdb\u884c\u6392\u5217\u7ec4\u5408\u3002\u4e5f\u5c31\u662f\u8bf4\u867d\u7136\u8fd9\u79cd\u7ec4\u5408\u6ca1\u6709\u89c1\u8fc7\uff0c\u4f46\u662f\u4ecd\u7136\u80fd\u60f3\u8c61\u51fa\u8fd9\u79cd\u7ec4\u5408\uff0c\u8fd9\u662f\u6cdb\u5316\u5f88\u91cd\u8981\u7684\u4e00\u4e2a\u7279\u70b9\u3002<\/span><\/section>\n

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\u56fe5. \u6df1\u5ea6\u5b66\u4e60\u4e2d\u7684\u56e0\u679c\u63a8\u7406<\/span><\/section>\n

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\u4ece\u56e0\u679c\u89d2\u5ea6\u6765\u770b\uff0c\u5f53\u4eca\u6df1\u5ea6\u5b66\u4e60\u793e\u533a\u7684\u4e00\u5927\u95ee\u9898\u5c31\u662f\u4ece\u56fe\u7247\u4e2d\u63d0\u53d6\u5230\u7684\u7279\u5f81\u90fd\u662f\u6df7\u6742\u5728\u4e00\u8d77\u7684\uff0c\u4f7f\u5f97\u56e0\u679c\u5173\u7cfb\u53ef\u80fd\u6210\u4e3a\u7a7a\u4e2d\u697c\u9601\uff0c\u963b\u788d\u56e0\u679c\u5173\u7cfb\u7684\u53d1\u5c55\u3002\u5982\u56fe5\u4e2d\u7684\u4e00\u5f00\u59cb\u5f97\u5230\u7684\u732b\u548c\u72d7\u7684\u7279\u5f81\u662f\u6df7\u6742\u7684\u3002\u4f46\u662f\u5982\u679c\u80fd\u505a\u5230\u7279\u5f81\u89e3\u8026\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u5f97\u5230\u5404\u4e2a\u7269\u4f53\u5206\u522b\u7684\u7279\u5f81\uff0c\u5e76\u4e14\u5728\u7279\u5f81\u5c42\u9762\u4e0a\u8fdb\u884cintervention\u548ccounterfactual\u7684\u64cd\u4f5c\uff0c\u4e0d\u7528\u518d\u53bb\u4fee\u6539\u56fe\u7247\u3002<\/span><\/section>\n

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3. \u5f97\u5230\u89e3\u8026\u7279\u5f81\u7684\u65b9\u6cd5<\/strong><\/strong><\/p>\n<\/section>\n

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\u7b2c\u4e00\u4e2a\u662fLabelled intervention\u3002\u8fd9\u65e9\u5728NIPS.2014\u5e74\u7684\u65f6\u5019\u5c31\u88ab\u63d0\u51fa[2]\uff0c\u8fd9\u79cd\u65b9\u6cd5\u5c31\u662f\u76f4\u63a5\u628a\u56e0\u7d20\u90fd\u6807\u51fa\u6765\u3002\u6bd4\u5982\u7ed9\u51fa\u4eba\u8138\u7684\u8eab\u4efd\u4fe1\u606f\u548c\u89d2\u5ea6\u4fe1\u606f\uff0c\u518d\u628a\u4eba\u8138\u7684\u8eab\u4efd\u548c\u89d2\u5ea6\u5728\u7279\u5f81\u5c42\u9762\u4e0a\u89e3\u8026\u51fa\u6765\u3002\u4f20\u7edf\u7684\u89e3\u8026\u65b9\u6cd5\u662f\u4f7f\u7528VAE\u7684encoder\u3001decoder\u7684\u6846\u67b6\uff0c\u4e5f\u5c31\u662f\u8bf4\u7ed9\u5b9a\u4e00\u5757\u56fe\u7247\uff0c\u5148\u901a\u8fc7encoder\u5f97\u5230\u4e00\u4e9b\u7279\u5f81\uff0c\u5e76\u4e14\u7279\u5f81\u91cc\u8eab\u4efd\u4fe1\u606f\u548c\u89c6\u89d2\u4fe1\u606f\u662f\u4e92\u76f8\u89e3\u8026\u7684\uff0c\u7136\u540e\u518d\u901a\u8fc7decoder\u628a\u56fe\u7247\u6062\u590d\u51fa\u6765\u3002<\/span><\/section>\n

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\u7b2c\u4e8c\u4e2a\u662fLabelled data<\/span>\uff08Supervised Learning\uff09<\/span>\u3002\u6bd4\u5982\u8bf4\u5206\u7c7b\u4efb\u52a1\u662f\u80fd\u8fbe\u5230\u90e8\u5206\u89e3\u8026\u7684\u4f5c\u7528\u7684\u3002\u5982\u56fe7\u4e2d\uff0c\u6a2a\u5411\u7684\u4e00\u884c\u90fd\u662f\u56fa\u5b9a\u7684\u7c7b\u522b\uff0c\u7eb5\u5411\u7684\u6bcf\u4e00\u5217\u90fd\u4ee3\u8868\u4e86\u4e0d\u540c\u7684\u5c5e\u6027\u7684\u90a3\u4e00\u7c7b\u3002<\/span><\/section>\n

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\u7b2c\u4e09\u4e2a\u662fAugmentations<\/span>\uff08Self-Supervised Learning\uff09<\/span>\u3002\u81ea\u76d1\u7763\u7684\u5b66\u4e60\u548c\u76d1\u7763\u5b66\u4e60\u6700\u5927\u7684\u4e0d\u540c\u5c31\u5728\u4e8e\u81ea\u76d1\u7763\u5b66\u4e60\u662f\u6ca1\u6709\u6807\u7b7e\u7684\u3002\u5982\u56fe8\uff0c\u81ea\u76d1\u7763\u5b66\u4e60\u4e2d\u628a\u6bcf\u4e00\u5f20\u56fe\u7247\u5f53\u505a\u4e00\u884c\uff0c\u6bcf\u4e00\u5217\u662f\u6bcf\u4e00\u4e2a\u56fe\u7247\u4e0d\u540c\u7684augmentations\uff0c\u5982\u88c1\u5207\uff0c\u989c\u8272\u53d8\u6362\u7b49\u3002<\/span><\/section>\n

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\u56fe9. \u603b\u7ed3\u4ee5\u4e0a\u7684\u4e09\u4e2a\u65b9\u6cd5\u53ef\u4ee5\u53d1\u73b0\uff0c\u5b83\u4eec\u90fd\u662f\u60f3\u628a\u6570\u636e\u4e2d\u7684\u884c\u5c5e\u6027\u548c\u5217\u5c5e\u6027\u5206\u5f00\u3002\u884c\u5c5e\u6027\u5f80\u5f80\u662f\u6211\u4eec\u60f3\u5f97\u5230\u7684\u5982\u7269\u4f53\u7c7b\u522b\u4fe1\u606f\uff0c\u800c\u5217\u5c5e\u6027\u5f80\u5f80\u662f\u6211\u4eec\u5e0c\u671b\u5ffd\u7565\u7684\u7279\u5f81\u5982\u89d2\u5ea6\u3002<\/span><\/p>\n


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4. \u7fa4\u8bba\u89c6\u89d2\u4e0b\u7684\u89e3\u8026\u7279\u5f81<\/strong><\/strong><\/p>\n<\/section>\n

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\u9996\u5148\u4ecb\u7ecd\u4e00\u4e0b\u7fa4\u8f68\u9053\u7684\u7406\u8bba\u3002\u7fa4\u5c31\u662f\u4e00\u79cdaction\uff0c\u662f\u4e00\u79cd\u4f5c\u7528\uff0c\u6bd4\u5982\u7fa4G\u7684\u4f5c\u7528\u53ef\u4ee5\u662f\u628a\u56fe\u7247\u987a\u65f6\u9488\u65cb\u8f6c30\u5ea6\u3002\u7fa4\u91cd\u590d\u4f5c\u7528\u5728\u56fe\u7247\u4e0a\u5f62\u6210\u7684\u4e00\u7cfb\u5217\u56fe\u7247\u7ec4\u6210\u7684\u96c6\u5408\u5c31\u53eb\u7fa4\u8f68\u9053\u3002<\/span><\/section>\n

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\u56fe10. \u6570\u636e\u5206\u7ec4\uff08Data Partition\uff09<\/span><\/section>\n

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\u56fe11. \u591a\u4e2a\u6570\u636e\u5206\u7ec4\u6784\u6210\u7684\u8f68\u9053<\/span><\/section>\n

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\u53ef\u4ee5\u628a\u6570\u636e\u96c6\u6309\u884c\u5c5e\u6027\u5206\u6210\u591a\u4e2a\u7ec4\uff0c\u6bcf\u4e2a\u7ec4\u5f62\u6210\u4e86\u4e00\u4e2a\u7fa4\u8f68\u9053\u3002\u5982\u56fe10\u4e2d\u5c31\u662f\u6309\u884c\u5c5e\u6027\u7269\u4f53\u7c7b\u522b\u5206\u7ec4\uff0c\u5f62\u6210\u4e86\u56fe11\u4e2d\u7684\u7fa4\u8f68\u9053\u3002\u800c\u4e8b\u5b9e\u4e0a\u6bcf\u4e00\u79cdData Partition\u90fd\u662f\u5bf9\u5e94\u4e00\u4e2a\u7279\u5b9a\u7684\u8bed\u4e49\u7684\uff0c\u5982\u7269\u4f53\u7c7b\u522b\u3002\u6240\u4ee5\u5982\u679c\u7ed9\u5b9a\u4e00\u4e2a\u7279\u5b9a\u7684\u4e00\u4e2aData Partition\uff0c\u5c31\u53ef\u4ee5\u628a\u5bf9\u5e94\u7684\u7279\u5b9a\u8bed\u4e49\u89e3\u8026\u51fa\u6765\u3002<\/span><\/section>\n

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\u90a3\u4e48\u5982\u4f55\u53bb\u89e3\u8026\u51fa\u8fd9\u4e2a\u7279\u5b9a\u7684\u8bed\u4e49\u5462\uff1f\u8fd9\u8981\u6c42contractive loss\u5bf9\u4e0d\u540c\u7684\u7ec4<\/span>\uff08\u8f68\u9053\uff09<\/span>\u662f\u5177\u6709\u4e0d\u53d8\u6027\u7684\u3002\u56fe12\u4e3e\u4e86\u4e00\u4e2a\u5f88\u76f4\u89c2\u7684\u4f8b\u5b50\u5e2e\u52a9\u5927\u5bb6\u7406\u89e3\u3002\u56fe\u4e2d\u6709\u4e24\u4e2a\u8f68\u9053\uff0c\u4e00\u4e2a\u5168\u90fd\u662f\u7eff\u8272\u7684\u6570\u5b57\uff0c\u53e6\u4e00\u4e2a\u5168\u662f\u7ea2\u8272\u7684\u6570\u5b57\u3002\u4e24\u4e2a\u8f68\u9053\u4e4b\u95f4\u7684\u5dee\u522b\u4ec5\u4ec5\u53ea\u6709\u7eff\u8272\u548c\u7ea2\u8272\u989c\u8272\u4e4b\u5206\uff0c\u5176\u4ed6\u7684\u8bed\u4e49\u90fd\u662f\u76f8\u540c\u7684\u3002\u60f3\u8981contractive loss\u5bf9\u4e24\u4e2a\u8f68\u9053\u4fdd\u6301\u4e0d\u53d8\uff0c\u8fd9\u610f\u5473\u7740\u9700\u8981\u628a\u6539\u53d8\u7684\u4e1c\u897f\u6620\u5c04\u5230\u56fa\u5b9a\u7684\u7ef4\u5ea6\u4e0a\u7136\u540e\u53bb\u9664\uff0c\u8fd9\u6837\u624d\u80fd\u5ffd\u7565\u53d8\u5316\u5e26\u6765\u7684\u5f71\u54cd\u3002\u6240\u4ee5\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\u4e00\u5b9a\u8981\u628a\u989c\u8272\u79fb\u5230\u7279\u5b9a\u7684\u7ef4\u5ea6\u4e0a\uff0c\u8fd9\u6837\u624d\u80fd\u53bb\u9664\u989c\u8272\u4e0d\u540c\u5e26\u6765\u7684\u5f71\u54cd\u3002\u5b9e\u9645\u4e0a\uff0c\u8fd9\u5c31\u662f\u628a\u989c\u8272\u8bed\u4e49\u63d0\u53d6\u51fa\u6765\u7684\u8fc7\u7a0b\u3002<\/span><\/section>\n

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\u5728\u6210\u529f\u63d0\u53d6\u4e00\u4e2a\u65b0\u7684\u8bed\u4e49\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u89e3\u8026\u51fa\u6765\u66f4\u591a\u7684\u5176\u4ed6\u8bed\u4e49\u3002\u56e0\u4e3a\u5f53\u63d0\u53d6\u7684\u7279\u5f81\u4e0d\u80fd\u5bf9\u67d0\u4e00\u4e2a\u5206\u7ec4\u4fdd\u6301\u4e0d\u53d8\u6027\u65f6\uff0c\u6211\u4eec\u5c31\u53d1\u73b0\u4e86\u65b0\u7684\u5206\u7ec4\uff0c\u53ef\u4ee5\u627e\u5230\u65b0\u7684\u8bed\u4e49\u3002\u8fd9\u4e5f\u5c31\u662f\u63a5\u4e0b\u6765\u8981\u8bb2\u7684IP-IRM\u7b97\u6cd5\u3002<\/span><\/section>\n

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\u56fe13. \u66f4\u65b0Feature Backbone<\/span><\/section>\n

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\u7b2c\u4e00\u5e03\u76f8\u5f53\u4e8e\u7ed9\u5b9a\u4e00\u4e2a\u56fa\u5b9a\u4e0d\u53d8\u7684\u7279\u5b9a\u7684\u5206\u7ec4\uff0c\u7136\u540e\u901a\u8fc7\u5982\u56fe13\u7684loss\u53bb\u5b66\u4e60\uff0c\u4e0d\u65ad\u66f4\u65b0backbone\uff0c\u5e0c\u671bbackbone\u53ef\u4ee5\u5b66\u5230\u628a\u7279\u5b9a\u7684\u8bed\u4e49\u63d0\u53d6\u51fa\u6765\u3002<\/span><\/section>\n

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\u56fe14. \u66f4\u65b0Data Partition<\/span><\/section>\n

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\u7b2c\u4e8c\u6b65\u6211\u4eec\u8981\u56fa\u5b9a\u4f4f\u5b66\u5230\u7684backbone\uff0c\u56e0\u4e3a\u8fd9\u65f6\u5019\u7279\u5f81\u5176\u5b9e\u76f8\u5f53\u4e8e\u5df2\u7ecf\u4fdd\u6301\u4e0d\u53d8\u4e86\uff0c\u6211\u4eec\u53ea\u662f\u60f3\u53bb\u5bfb\u627e\u5230\u76ee\u524d\u7279\u5f81\u91cc\u9762\u8fd8\u6709\u54ea\u4e9b\u8bed\u4e49\u6ca1\u6709\u88ab\u63ed\u9732\u51fa\u6765\uff0c\u6240\u4ee5\u8fd9\u65f6\u5019\u6211\u4eec\u8981\u7528\u7279\u5f81\u53bb\u53cd\u5411\u589e\u5927loss\uff0c\u4ece\u800c\u627e\u5230\u8fd8\u8fbe\u4e0d\u5230\u4f18\u5316\u76ee\u6807\u7684\u5206\u7ec4\uff0c\u7136\u540e\u76f8\u5f53\u4e8e\u627e\u5230\u4e86\u4e00\u4e2a\u65b0\u7684\u8bed\u4e49\u3002\u6240\u4ee5\u6211\u4eec\u5728\u8fd9\u4e00\u6b65\u662f\u8981\u66f4\u65b0partition\u5e76\u56fa\u5b9afeature backbone\u3002<\/span><\/section>\n

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\u4e0b\u9762\u7b80\u5355\u5730\u5217\u4e3e\u4e86\u4e00\u4e9b\u6709\u8da3\u7684\u5b9e\u9a8c\u7ed3\u679c\u3002<\/span><\/section>\n

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\u56fe15. SimCLR\u4e0eIP-IRM\u5728CMNIST\u4e0a\u53d6\u5f97\u7684\u7279\u5f81\u7a7a\u95f4\u5bf9\u6bd4\uff0cIP-IRM\u83b7\u53d6\u7684\u7279\u5f81\u662f\u4e2d\u5fc3\u5bf9\u79f0\u7684\uff0c\u4f53\u73b0\u4e86\u8bed\u4e49\u7279\u5f81\u662f\u89e3\u8026\u7684<\/span><\/p>\n


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\u56fe16. \u5bf9\u6bcf\u4e2a\u7279\u5f81\u7ef4\u5ea6\u7684\u53ef\u89c6\u5316\uff0cIP-IRM\u5f97\u5230\u7684\u4e0d\u540c\u7ef4\u5ea6\u4ee3\u8868\u4e86\u4e0d\u540c\u7684\u8bed\u4e49<\/span><\/section>\n

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\u56fe17. \u6570\u636e\u5206\u7ec4\u7684\u53ef\u89c6\u5316\uff0c\u5206\u7ec43\u4e3a\u6309\u503e\u659c\u89d2\u5ea6\u8fdb\u884c\u5206\u7ec4\u7684\u65b0\u7684\u5206\u7ec4<\/span><\/section>\n

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\u56fe18. \u52a0\u957f\u8bad\u7ec3\u65f6\u95f4\u540e\uff0c\u7ed3\u679c\u582a\u6bd4\u76d1\u7763\u5b66\u4e60<\/span><\/section>\n

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\u53c2\u8003\u6587\u732e<\/span><\/strong><\/span><\/section>\n

1. Suter,Raphael,et al.\u201cRobostly disentangled causal mechanisms:Validating deep representations for interventional robustness.\u201dICML\u201919<\/span><\/p>\n

2. Zhu,Zhenyao,et al.\u201cMulti-view perceptron:a deep model for learning face identity and view representations.\u201dNIPS.2014.<\/span><\/p>\n


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\u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a\u7b2c\u4e09\u5b63\u542f\u52a8<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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\u7531\u667a\u6e90\u793e\u533a\u3001\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u4e3e\u529e\u7684\u56e0\u679c\u79d1\u5b66\u4e0eCausal AI\u8bfb\u4e66\u4f1a\u7b2c\u4e09\u5b63<\/a>\uff0c<\/span>\u5c06\u4e3b\u8981\u9762\u5411\u4e24\u7c7b\u4eba\u7fa4\uff1a\u5982\u679c\u4f60\u4ece\u4e8b\u8ba1\u7b97\u673a\u76f8\u5173\u65b9\u5411\u7814\u7a76\uff0c\u5e0c\u671b\u4e3a\u4e0d\u540c\u9886\u57df\u5f15\u5165\u65b0\u7684\u8ba1\u7b97\u65b9\u6cd5\uff0c\u901a\u8fc7\u5927\u6570\u636e\u3001\u65b0\u7b97\u6cd5\u5f97\u5230\u65b0\u6210\u679c\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bfb\u4e66\u4f1a\u5404\u4e2a\u9886\u57df\u7684\u6838\u5fc3\u56e0\u679c\u95ee\u9898\u4ecb\u7ecd\u548c\u8bba\u6587\u63a8\u8350\u5feb\u901f\u5165\u624b\uff1b\u5982\u679c\u4f60\u4ece\u4e8b\u5176\u4ed6\u7406\u5de5\u79d1\u6216\u4eba\u6587\u793e\u79d1\u9886\u57df\u7814\u7a76\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7\u6240\u5c5e\u9886\u57df\u7684\u56e0\u679c\u7814\u7a76\u7efc\u8ff0\u4ecb\u7ecd\u548c\u7814\u8ba8\u5df2\u6709\u5de5\u4f5c\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5728\u81ea\u5df1\u7684\u7814\u7a76\u4e2d\u5feb\u901f\u5f00\u59cb\u5c1d\u8bd5\u90e8\u7f72\u7ed3\u5408\u56e0\u679c\u7684\u7b97\u6cd5\u3002\u8bfb\u4e66\u81ea2021\u5e7410\u670824\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u65e5\u4e0a\u5348 10:00-12:00\u4e3e\u529e\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba1 2-3 \u4e2a\u6708\u3002<\/span><\/p>\n


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\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/p>\n

\u56e0\u679c+X\uff1a\u89e3\u51b3\u591a\u5b66\u79d1\u9886\u57df\u7684\u56e0\u679c\u95ee\u9898 | \u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a\u7b2c\u4e09\u5b63\u542f\u52a8<\/a><\/span><\/strong><\/span><\/p>\n

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<\/p>\n<\/section>\n


<\/p>\n

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\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/p>\n