{"id":59535,"date":"2020-10-14T13:46:04","date_gmt":"2020-10-14T04:46:04","guid":{"rendered":"https:\/\/smilegate.ai\/?p=59535"},"modified":"2020-11-30T09:35:22","modified_gmt":"2020-11-30T00:35:22","slug":"meal-v2","status":"publish","type":"post","link":"https:\/\/smilegate.ai\/cn\/2020\/10\/14\/meal-v2\/","title":{"rendered":"\u81b3\u98dfv2\uff1a\u4f7f\u7528ResNet-50\u5b9e\u73b0ImageNet Top-1 80%"},"content":{"rendered":"

\u5c06\u591a\u4e2a\u7f51\u7edc\u6a21\u578b\u7ec4\u5408\u5728\u4e00\u8d77\u53ef\u4ee5\u63d0\u9ad8\u6027\u80fd\uff0c\u4f46\u73b0\u5b9e\u60c5\u51b5\u662f\uff0c\u7531\u4e8e\u603b\u7f51\u7edc\u89c4\u6a21\u548c\u63a8\u7406\u65f6\u95f4\u4e5f\u4f1a\u589e\u52a0\uff0c\u56e0\u6b64\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u5b58\u5728\u8bb8\u591a\u56f0\u96be\u3002<\/p>\n\n\n\n

MEAL\uff08\u901a\u8fc7\u5bf9\u6297\u5b66\u4e60\u7684\u591a\u6a21\u578b\u96c6\u6210\uff09\u5e94\u7528\u5e08\u751f\u5b66\u4e60\u6765\u89e3\u51b3\u6b64\u95ee\u9898\uff0c\u5177\u4f53\u6765\u8bf4\uff0c\u5b83\u8003\u8651\u4e86\u5df2\u7ecf\u4f5c\u4e3a\u591a\u4f4d\u8001\u5e08\u5b66\u4e60\u7684\u591a\u4e2a\u7f51\u7edc\uff0c\u5e76\u901a\u8fc7\u9009\u62e9\u6a21\u5757\u9009\u62e9\u4e86\u5176\u4e2d\u7684\u4e00\u4e2a\u3002\u5e76\u6700\u7ec8\u84b8\u998f\u51fa\u4e00\u4e2a\u5355\u4e00\u7684\u5b66\u751f\u7f51\u7edc\u3002<\/p>\n\n\n\n

\u5171\u4eab\u94fe\u63a5\u662fMEAL v2 github\uff0c\u5e76\u4e14\u5df2\u5e94\u7528\u4e86\u4e00\u4e9b\u6539\u8fdb\uff0c\u4f8b\u5982\u76f4\u63a5\u4f7f\u7528\u6559\u5e08\u7684\u96c6\u5408\u800c\u4e0d\u662f\u9009\u62e9\u6a21\u5757\uff0c\u5e76\u4e14\u4ece\u84b8\u998f\u8fc7\u7a0b\u4e2d\u6392\u9664\u4e86\u4e00\u4e2a\u70ed\u6807\u7b7e\/\u786c\u6807\u7b7e\u3002\u7ed3\u679c\uff0c\u5728\u4e0d\u66f4\u6539\u7f51\u7edc\u7ed3\u6784\u7684\u60c5\u51b5\u4e0b\uff0cResNet-50\u7684ImageNet-1K\u6027\u80fd\uff08224\u00d7224\u5355\u4f5c\u7269\uff09\u8d85\u8fc7\u4e86TOP-1 80%\u3002\u8be5\u8bba\u6587\u7684\u6807\u9898\u4e5f\u662f\u201c ResNet-50\u6027\u80fd80%\u901a\u8fc7\u800c\u6ca1\u6709\u82b1\u62db\u201d\u3002 <\/p>\n\n\n\n

\u8be5\u4ee3\u7801\u4ee5\u53caResNet-50\uff0cMobileNet V3\u548cEfficientNet-B0\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u90fd\u53ef\u4ee5\u516c\u5f00\u83b7\u5f97\uff0c\u56e0\u6b64\u770b\u8d77\u6765\u975e\u5e38\u9002\u5408\u8fc1\u79fb\u5b66\u4e60\u3002<\/p>\n\n\n\n

<\/div>\n\n\n
<\/a>
\"\"<\/div><\/div>
szq0214 \/ MEAL-V2<\/div>
\u9910\u70b9V2\uff1a\u5728\u6ca1\u6709\u6280\u5de7\u7684\u60c5\u51b5\u4e0b\u5c06ImageNet\u4e0a\u7684Vanilla ResNet-50\u63d0\u9ad8\u523080% + Top-1\u7cbe\u5ea6\u2013 szq0214 \/ MEAL-V2<\/div><\/div><\/div>
<\/span><\/div>","protected":false},"excerpt":{"rendered":"

\uc5ec\ub7ec \ub124\ud2b8\uc6cc\ud06c \ubaa8\ub378\uc744 \uc559\uc0c1\ube14\ub85c \uacb0\ud569\ud558\uba74 \uc131\ub2a5\uc774 \uc624\ub974\ub294\ub370, \ucd1d \ub124\ud2b8\uc6cc\ud06c\uc758 \ud06c\uae30\uc640 \ucd94\ub860 \uc2dc\uac04\ub3c4 \ud568\uaed8 \uc99d\uac00\ud558\uae30 \ub54c\ubb38\uc5d0 \uc2e4\uc81c\ub85c \uc801\uc6a9\ud560 \ub54c\ub294 \uc5b4\ub824\uc6c0\uc774 \ub9ce\uc740 \uac83\uc774 \ud604\uc2e4\uc785\ub2c8\ub2e4. MEAL(Multi-model Ensemble via Adversarial Learning)\uc740 \uc774\ub7ec\ud55c \uc810\uc744 \ud574\uacb0\ud558\uae30 \uc704\ud574 Teacher-Student learning\uc744 \uc801\uc6a9\ud558\ub294\ub370, \uad6c\uccb4\uc801\uc73c\ub85c\ub294 \uc774\ubbf8 \ud559\uc2b5\ub41c \uc5ec\ub7ec \uac1c\uc758 \ub124\ud2b8\uc6cc\ud06c\ub97c \ubcf5\uc218\uc758 teacher\ub77c\uace0 \ubcf4\uace0, selection module\uc5d0 \uc758\ud574 \uadf8 \uc911 \ud558\ub098\ub97c \uc120\ud0dd\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec distillation \uc2dc\ud0b4\uc73c\ub85c\uc368 \ucd5c\uc885\uc801\uc73c\ub85c\ub294 \ub2e8\uc77c…<\/p>\n

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