{"id":7984,"date":"2017-01-12T01:00:00","date_gmt":"2017-01-11T17:00:00","guid":{"rendered":"http:\/\/swarma.org\/%swarma-paper%\/"},"modified":"2018-12-06T08:19:59","modified_gmt":"2018-12-06T00:19:59","slug":"%e6%88%91%e7%9a%84%e8%82%a9%e4%b8%8a%e6%98%af%e9%a3%8e%ef%bc%8c%e9%a3%8e%e4%b8%8a%e6%98%af%e9%97%aa%e7%83%81%e7%9a%84%e6%98%9f%e7%be%a4-%e5%8e%9f%e5%88%9b%e7%b2%be%e9%80%89","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=7984","title":{"rendered":"\u6211\u7684\u80a9\u4e0a\u662f\u98ce\uff0c\u98ce\u4e0a\u662f\u95ea\u70c1\u7684\u661f\u7fa4 | \u539f\u521b\u7cbe\u9009"},"content":{"rendered":"
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\u674e\u5ae3\u7136<\/span><\/strong><\/p>\n

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


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\u201cWhatI cannot create, I do not understand.\u201d <\/span><\/p>\n

\u2014\u2014 Richard Feynman<\/span><\/p>\n

\"\u96c6\u667a\"
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\u53e6\u4e00\u7bc7\u91cd\u91cf\u7ea7\u7684\u5173\u4e8e GAN \u8bad\u7ec3\u6280\u5de7\u7684\u7814\u7a76\u7684\u5de5\u4f5c\u4fbf\u662fDeepConvolutional Generative Adversarial Networks\uff08DCGAN\uff09[6]\u3002[6] \u4e2d\u603b\u7ed3\u4e86\u8bb8\u591a\u5bf9\u4e8e GAN \u8fd9\u7684\u7f51\u7edc\u7ed3\u6784\u8bbe\u8ba1\u548c\u9488\u5bf9 CNN \u8fd9\u79cd\u7f51\u7edc\u7684\u8bad\u7ec3\u7ecf\u9a8c\u3002\u6bd4\u5982\uff0c\u4ed6\u4eec\u7528strided convolutional networks \u66ff\u4ee3\u4f20\u7edf CNN \u4e2d\u7684 pooling \u5c42\uff0c\u4ece\u800c\u5c06GAN \u4e2d\u7684\u751f\u6210\u6a21\u578b\uff08G\uff09\u53d8\u6210\u4e86 fully differentiable \u7684\uff0c\u7ed3\u679c\u4f7f\u5f97GAN \u7684\u8bad\u7ec3\u66f4\u52a0\u7a33\u5b9a\u548c\u53ef\u63a7\u3002<\/span><\/p>\n

\u4e3a\u4e86\u63d0\u9ad8\u8bad\u7ec3\u7684\u7a33\u5b9a\u6027\uff0c\u53e6\u4e00\u4e2a\u5f88\u81ea\u7136\u7684\u89d2\u5ea6\u5c31\u662f\u6539\u53d8\u5b66\u4e60\u65b9\u6cd5\u3002\u628a\u7eaf\u65e0\u76d1\u7763\u7684 GAN \u53d8\u6210\u534a\u76d1\u7763\u6216\u8005\u6709\u76d1\u7763\u7684\u3002\u8fd9\u4fbf\u53ef\u4ee5\u4e3a GAN \u7684\u8bad\u7ec3\u52a0\u4e0a\u4e00\u70b9\u70b9\u675f\u7f1a\uff0c\u6216\u8005\u8bf4\u52a0\u4e0a\u4e00\u70b9\u70b9\u76ee\u6807\u3002[2] \u4e2d\u63d0\u51fa\u7684Conditional Generative Adversarial Nets\uff08CGAN\uff09\u4fbf\u662f\u5341\u5206\u76f4\u63a5\u7684\u6a21\u578b\u6539\u53d8\uff0c\u5728\u751f\u6210\u6a21\u578b\uff08G\uff09\u548c\u5224\u522b\u6a21\u578b\uff08D\uff09\u7684\u5efa\u6a21\u4e2d\u5747\u5f15\u5165 conditionalvariable y\uff0c\u8fd9\u4e2a y \u5c31\u662f\u6570\u636e\u7684\u4e00\u79cd label\u3002\u4e5f\u56e0\u6b64\uff0cCGAN \u53ef\u4ee5\u770b\u505a\u628a\u65e0\u76d1\u7763\u7684 GAN \u53d8\u6210\u6709\u76d1\u7763\u7684\u6a21\u578b\u7684\u4e00\u79cd\u6539\u8fdb\u3002\u8fd9\u4e2a\u7b80\u5355\u76f4\u63a5\u7684\u6539\u8fdb\u88ab\u8bc1\u660e\u975e\u5e38\u6709\u6548\uff0c\u5e76\u5e7f\u6cdb\u7528\u4e8e\u540e\u7eed\u7684\u76f8\u5173\u5de5\u4f5c\u4e2d\u3002<\/span><\/p>\n

\"\u96c6\u667a\"
\u7b2c\u4e09\u79cd\u6539\u8fdb GAN\u8fc7\u4e8e\u81ea\u7531\u7684\u601d\u8def\uff0c\u548c\u7b2c\u4e00\u79cd\u4f1a\u6bd4\u8f83\u76f8\u4f3c\u3002\u65e2\u7136\u592a\u96be\u63a7\u5236 GAN \u7684\u5b66\u4e60\uff0c\u4e0d\u5982\u6211\u4eec\u5c31\u62c6\u89e3\u4e00\u4e0b\uff0c\u4e0d\u8981\u8ba9 GAN \u4e00\u6b21\u5b66\u5b8c\u5168\u90e8\u7684\u6570\u636e\uff0c\u800c\u662f\u8ba9 GAN \u4e00\u6b65\u6b65\u5b8c\u6210\u8fd9\u4e2a\u5b66\u4e60\u8fc7\u7a0b\u3002\u5177\u4f53\u5230\u56fe\u7247\u751f\u6210\u6765\u8bf4\u5c31\u662f\uff0c\u4e0d\u8981\u8ba9 GAN \u4e2d\u7684\u751f\u6210\u6a21\u578b\uff08G\uff09\u6bcf\u6b21\u90fd\u76f4\u63a5\u751f\u6210\u4e00\u6574\u5f20\u56fe\u7247\uff0c\u800c\u662f\u8ba9\u5b83\u751f\u6210\u56fe\u7247\u7684\u4e00\u90e8\u5206\u3002\u8fd9\u4e2a\u601d\u60f3\u53ef\u4ee5\u8ba4\u4e3a\u662fDeepMind\u4e5f\u5f88\u6709\u540d\u7684\u5de5\u4f5c DRAW \u7684\u4e00\u79cd\u53d8\u5f62\u3002DRAW \u7684\u8bba\u6587[3] \u5f00\u7bc7\u5c31\u8bf4\uff0c\u6211\u4eec\u4eba\u7c7b\u5728\u7ed8\u5236\u4e00\u5f20\u56fe\u7247\u65f6\uff0c\u5f88\u5c11\u662f\u4e00\u7b14\u5b8c\u6210\u7684\u3002\u65e2\u7136\u6211\u4eec\u4eba\u7c7b\u90fd\u4e0d\u662f\u8fd9\u6837\uff0c\u4e3a\u4ec0\u4e48\u6211\u4eec\u8981\u5bc4\u5e0c\u671b\u4e8e\u673a\u5668\u53ef\u4ee5\u505a\u5230\u5462\uff1f\u8bba\u6587[4] \u4e2d\u63d0\u51fa\u7684 LAPGAN \u5c31\u662f\u57fa\u4e8e\u8fd9\u4e2a\u601d\u60f3\uff0c\u5c06 GAN \u7684\u5b66\u4e60\u8fc7\u7a0b\u53d8\u6210\u4e86 sequential \u201c\u5e8f\u5217\u5f0f\u201d\u7684\u3002\u5177\u4f53\u4e0a\uff0cLAPGAN \u91c7\u7528\u4e86Laplacian Pyramid \u5b9e\u73b0\u4e86\u201c\u5e8f\u5217\u5316\u201d\uff0c\u4e5f\u56e0\u6b64\u8d77\u540d\u505a LAPGAN\u3002\u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u8fd9\u4e2a LAPGAN \u4e2d\u4e5f\u6709\u201c\u6b8b\u5dee\u201d\u5b66\u4e60\u7684\u601d\u60f3\uff08\u4e0e\u540e\u6765\u5927\u706b\u7684ResNet\u4e5f\u7b97\u662f\u6709\u4e00\u70b9\u5173\u8054\uff09\u3002\u5728\u5b66\u4e60\u5e8f\u5217\u4e2d\uff0cLAPGAN \u4e0d\u65ad\u5730\u8fdb\u884cdownsample\u548cupsample\u64cd\u4f5c\uff0c\u7136\u540e\u5728\u6bcf\u4e00\u4e2a Pyramid level \u4e2d\uff0c\u53ea\u5c06\u6b8b\u5dee\u4f20\u9012\u7ed9\u5224\u522b\u6a21\u578b\uff08D\uff09\u8fdb\u884c\u5224\u65ad\u3002\u8fd9\u6837\u7684 sequential + \u6b8b\u5dee\u7ed3\u5408\u7684\u65b9\u5f0f\uff0c\u80fd\u6709\u6548\u51cf\u5c11 GAN \u9700\u8981\u5b66\u4e60\u7684\u5185\u5bb9\u548c\u96be\u5ea6\uff0c\u4ece\u800c\u8fbe\u5230\u4e86\u201c\u8f85\u52a9\u201d GAN \u5b66\u4e60\u7684\u76ee\u7684\u3002<\/span><\/p>\n

\"\u96c6\u667a\"
\u53e6\u4e00\u4e2a\u57fa\u4e8e sequential \u601d\u60f3\u53bb\u6539\u8fdb GAN \u7684\u5de5\u4f5c\u6765\u81ea\u4e8e[5] \u4e2d\u7684 GRAN\u3002\u4e0e LAPGAN[4] \u6bcf\u4e00\u4e2a sequential step\uff08Pyramid level\uff09\u90fd\u662f\u72ec\u7acb\u8bad\u7ec3\u7684\u4e0d\u540c\u7684\u662f\uff0cGRAN \u628a GAN \u548c LSTM \u7ed3\u5408\uff0c\u8ba9 sequence \u4e2d\u7684\u6bcf\u4e00\u6b65\u5b66\u4e60\u548c\u751f\u6210\u80fd\u5145\u5206\u5229\u7528\u4e0a\u4e00\u6b65\u7684\u7ed3\u679c\u3002\u5177\u4f53\u4e0a\u6765\u770b\uff0cGRAN \u7684\u6bcf\u4e00\u6b65\u90fd\u6709\u4e00\u4e2a\u50cf LSTM \u4e2d\u7684 cell\uff0cC_t\uff0c\u5b83\u51b3\u5b9a\u4e86\u6bcf\u4e00\u6b65\u751f\u6210\u7684\u5185\u5bb9\u548c\u7ed3\u679c\uff1bGRAN \u4e2d\u7684 h_{c,t} \u4e5f\u5982 LSTM \u4e00\u6837\uff0c\u4ee3\u8868\u7740 hidden states\u3002\u65e2\u7136\u662f\u7ed3\u5408 LSTM \u548c GAN\uff0c\u90a3\u4e48\u8bf4\u5b8c\u4e86 LSTM \u65b9\u9762\u7684\u5f15\u5165\uff0c\u4fbf\u662f GAN \u65b9\u9762\u7684\u4e86\u3002GRAN \u5c06 GAN \u4e2d\u751f\u6210\u6a21\u578b\uff08G\uff09\u7684\u5148\u9a8c\u4e5f\u8fdb\u884c\u4e86\u5efa\u6a21\uff0c\u53d8\u6210\u4e86 hidden of prior h_z\uff1b\u7136\u540e\u5c06 h_z\u548c h_{c,t} \u62fc\u63a5\uff08concatenate\uff09\u4e4b\u540e\u4f20\u9012\u7ed9\u6bcf\u4e00\u6b65\u7684C_t\u3002<\/span><\/p>\n

\"\u96c6\u667a\"
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\"\u96c6\u667a\"<\/p>\n

\"\u96c6\u667a\"
\u7136\u800c\u5b9e\u9645\u4e0a\uff0cinfoGAN\u5e76\u4e0d\u662f\u7b2c\u4e00\u4e2a\u5c06\u4fe1\u606f\u8bba\u7684\u89d2\u5ea6\u5f15\u5165 GAN \u6846\u67b6\u7684\u5de5\u4f5c\u3002\u8fd9\u662f\u56e0\u4e3a\uff0c\u5728infoGAN\u4e4b\u524d\uff0c\u8fd8\u6709\u4e00\u4e2a\u53eb\u505a f-GAN[8] \u7684\u5de5\u4f5c\u3002\u5e76\u4e14\uff0cGAN \u672c\u8eab\u4e5f\u53ef\u4ee5\u4ece\u4fe1\u606f\u8bba\u89d2\u5ea6\u53bb\u89e3\u91ca\u3002\u5982\u672c\u6587\u5f00\u7bc7\u6240\u8bf4\uff0c\u5728\u539f\u59cb GAN \u8bba\u6587[1] \u4e2d\uff0c\u4f5c\u8005\u662f\u901a\u8fc7\u535a\u5f08\u8bba\u7684\u89d2\u5ea6\u89e3\u91ca\u4e86 GAN \u7684\u601d\u60f3\u3002\u7136\u800c\uff0cGAN \u7684\u751f\u6210\u6a21\u578b\uff08G\uff09\u4ea7\u751f\u7684\u6570\u636e\u548c\u771f\u5b9e\u6570\u636e\u5c31\u53ef\u4ee5\u770b\u505a\u4e00\u9897\u786c\u5e01\u7684\u4e24\u9762\u3002\u5f53\u629b\u786c\u5e01\u629b\u5230\u6b63\u9762\u65f6\uff0c\u6211\u4eec\u5c31\u5c06\u4e00\u4e2a\u771f\u5b9e\u6570\u636e\u6837\u672c\u5c55\u793a\u7ed9\u5224\u522b\u6a21\u578b\uff08D\uff09\uff1b\u53cd\u4e4b\uff0c\u5219\u5c55\u793a\u7531\u751f\u6210\u6a21\u578b\uff08G\uff09\u751f\u6210\u7684\u201c\u5047\u201d\u6837\u672c\u3002\u800c GAN \u7684\u7406\u60f3\u72b6\u6001\u662f\uff0c\u5224\u522b\u6a21\u578b\uff08D\uff09\u5bf9\u4e8e\u786c\u5e01\u7684\u5224\u65ad\u51e0\u4e4e\u7b49\u540c\u4e8e\u968f\u673a\uff0c\u4e5f\u5c31\u662f\u751f\u6210\u6a21\u578b\uff08G\uff09\u4ea7\u751f\u7684\u6570\u636e\u5b8c\u5168\u7b26\u5408\u771f\u5b9e\u6570\u636e\u3002\u90a3\u4e48\u8fd9\u65f6\u5019\uff0cGAN \u7684\u8bad\u7ec3\u8fc7\u7a0b\u5b9e\u9645\u5728\u505a\u7684\u5c31\u662f\u6700\u5c0f\u5316\u8fd9\u9897\u786c\u5e01\u548c\u771f\u5b9e\u6570\u636e\u4e4b\u95f4\u7684\u4e92\u4fe1\u606f\u3002\u4e92\u4fe1\u606f\u8d8a\u5c0f\uff0c\u5224\u522b\u6a21\u578b\uff08D\uff09\u80fd\u4ece\u89c2\u5bdf\u4e2d\u83b7\u5f97\u7684\u4fe1\u606f\u8d8a\u5c11\uff0c\u4e5f\u5c31\u8d8a\u53ea\u80fd\u50cf\u201c\u968f\u673a\u201d\u4e00\u6837\u731c\u7ed3\u679c\u3002\u65e2\u7136\u6709\u4e86\u8fd9\u6837\u4e00\u4e2a\u4ece\u4e92\u4fe1\u606f\u89d2\u5ea6\u7684\u5bf9\u4e8e GAN \u7684\u7406\u89e3\uff0c\u90a3\u4e48\u662f\u5426\u80fd\u5bf9 GAN \u8fdb\u884c\u66f4\u8fdb\u4e00\u6b65\u7684\u6539\u9020\u5462\uff1f\u5176\u5b9e\u662f\u53ef\u4ee5\u7684\u3002\u6bd4\u5982\u53ef\u4ee5\u628a\u9488\u5bf9\u4e92\u4fe1\u606f\u7684\u5efa\u6a21\u66f4\u8fdb\u4e00\u6b65\u5730\u6cdb\u5316\u4e3a\u57fa\u4e8e divergence \u7684\u4f18\u5316\u76ee\u6807\u3002\u8fd9\u65b9\u9762\u7684\u8ba8\u8bba\u548c\u6539\u8fdb\u53ef\u4ee5\u89c1\u8bba\u6587[8]\uff0cf-GAN\u3002<\/span><\/p>\n

\u5c3d\u7ba1\u63a5\u4e0b\u6765\u8981\u4ecb\u7ecd\u7684\u4e24\u4e2a\u5de5\u4f5c\uff0c\u662f\u5728\u7814\u8bfb\u8425\u4e4b\u540e\u624d\u53d1\u8868\u7684\uff0c\u4f46\u4e5f\u548c\u8fd9\u6b21\u6211\u5728\u7814\u8bfb\u8425\u5206\u4eab\u7684\u5185\u5bb9\u606f\u606f\u76f8\u5173\u3002\u5176\u4e2d\u4e00\u4efd\u5de5\u4f5c[9]\u5c31\u53ef\u4ee5\u770b\u505a\u662f f-GAN \u7684\u5ef6\u7eed\u3002\u8fd9\u7bc7\u5de5\u4f5c\u53ef\u4ee5\u88ab\u770b\u505a\u662f\u628a\u4ee5\u524d\u7684 GAN training tricks \u7edf\u4e00\u5230\u4e00\u4e2a\u6846\u67b6\u4e0b\u3002\u4ece[9]\u7684\u9898\u76ee\u6765\u770b\uff0c\u8fd9\u4e2a multi \u662f\u6307\u5728\u4ed6\u4eec\u7684\u6846\u67b6\u91cc\uff0c\u4e00\u4e2a GAN \u53ef\u4ee5\u6709\u591a\u4e2aD\uff0c\u4f46\u672c\u8d28\u4e0a\u8fd8\u662f\u4e00\u4e2aG\u3002\u800c\u8fd9\u591a\u4e2a D \u5176\u5b9e\u6700\u7ec8\u4e5f\u8fd8\u662f\u4f1a\u201c\u6c47\u96c6\u201d\u6210\u4e00\u4e2a\uff0c\u6765\u7ed9D\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u56de\u5f52\u5230\u53cc\u65b9\u535a\u5f08\u3002\u770b\u4e0b\u56fe\uff1a<\/span><\/p>\n

\"\u96c6\u667a\"\u8fd9\u91cc\u7684V(D_i, G) \u5c31\u662fi\u4e2a discriminator\uff0cD_i\u548c\u540c\u4e00\u4e2a generator G \u5148\u5206\u522b\u8bad\u7ec3\uff1b\u7136\u540e F(\u00b7) \u8fd9\u4e2a\u51fd\u6570\u76f8\u5f53\u4e8e\u90a3\u4e2a\u201c\u6c47\u96c6\u201d\u51fd\u6570\u3002\u5f53\u6211\u4eec\u7b80\u5355\u7684\u7528 F := max \u65f6\uff0c\u5c31\u76f8\u5f53\u4e8e\u662f\u4ece\u8fd9i\u4e2aD_i\u91cc\u9009\u62e9\u6700\u597d\u7684\u90a3\u4e2a\uff0cbest\uff0c\u6700discrminative\uff0c\u5f53\u7136\u4e5f\u662f\u6700 harsh \u7684\u4e00\u4e2a\uff0c\u6765\u7ed9\u6700\u7ec8\u7684 G \u7528\u6765\u8bad\u7ec3\uff08\u8fd8\u662f\u6700\u521d\u7684\u90a3\u4e2a G\uff09\u3002\u4f46\u662f\uff0c\u4e00\u4e2a\u7279\u522b harsh \u7684 D\uff0c\u53ef\u80fd\u5bf9\u4e8eG\u7684\u8bad\u7ec3\u662f\u4e0d\u597d\u7684\u3002\u8fd9\u4e2a\u4e5f\u662f\u8fc7\u53bb\u4e00\u76f4\u5f3a\u8c03\u7684\uff0c\u5728\u8bad\u7ec3 GAN \u65f6\uff0c\u9700\u8981 G,D \u5e73\u8861\u3002\u4efb\u4f55\u4e00\u65b9\u592a\u5f3a\uff0c\u90fd\u4f1a\u5bfc\u81f4\u8bad\u7ec3 collapse\u3002\u4e5f\u5c31\u662f\u535a\u5f08\u6e38\u620f\u73a9\u4e0d\u4e0b\u53bb\u4e86\u3002\u6240\u4ee5\u8bf4\uff0c\u672c\u8d28\u4e0a\u4ed6\u4eec\u8fd9\u4e2a F(\u00b7) \u8fd8\u662f\u6ca1\u9003\u79bb\u8fd9\u4e2a\u601d\u60f3\uff0c\u662f\u4e3a G \u627e\u4e00\u4e2a\u66f4\u201c\u5e73\u8861\u201d\u7684\u66f4\u201c\u5408\u9002\u201d\u7684\u5bf9\u624b D\u3002\u7136\u540e\uff0c\u53c8\u56e0\u4e3a\u8fd8\u662f\u901a\u8fc7\u591a\u4e2a discriminator \u4e00\u8d77\u518d\u505a regularize \u6765\u5b9e\u73b0\u8fd9\u4e2a\u201c\u5e73\u8861\u201d\u3002\u9057\u7559\u95ee\u9898\u662f\uff0c\u4ed6\u4eec\u6ca1\u6709\u5bf9 V(D_i, G) \u8fd9\u4e2a V \u505a\u9009\u62e9\uff0c\u5176\u5b9e\u8fd9\u4e2a V \u7684\u9009\u62e9\u624d\u662f GAN \u4e2d\u66f4\u7406\u8bba\u7684\u6539\u9020\uff0c\u4e5f\u5c31\u662f\u76f4\u63a5\u628aminmax alternating objective \u6539\u9020\u6389\uff0c\u8ba9\u8fd9\u4e2a\u76ee\u6807\u7684\u6700\u4f18\u89e3\u66f4\u63a5\u8fd1\u771f\u5b9e\u6700\u4f18\u89e3\u3002\u8fd9\u65b9\u9762\u7684\u5de5\u4f5c\u5176\u5b9e\u4e5f\u662f\u6709\u7684\u3002<\/span>
<\/p>\n

\u6700\u65b0\u7684\u5173\u4e8e GAN \u7684\u6539\u8fdb\uff0c\u5219\u6765\u81ea\u4e8e Google \u7684DeepMind\u56e2\u961f[10]\u3002\u8fd9\u7bc7\u6587\u7ae0\u867d\u7136\u53d1\u8868\u4e8e\u7814\u8bfb\u8425\u4e4b\u540e\uff0c\u4f46\u4f5c\u4e3a\u7814\u8bfb\u8425\u7684\u540e\u7eed\u611f\u60f3\uff0c\u6211\u4e5f\u5e0c\u671b\u80fd\u8865\u5145\u8fdb\u6765\u3002\u601d\u60f3\u975e\u5e38\u76f4\u89c2\uff0c\u7f3a\u70b9\u80af\u5b9a\u5c31\u662f computation expensive\u3002GAN \u7684\u8bad\u7ec3\u4e0d\u7a33\u5b9a\u4e3b\u8981\u662f\u56e0\u4e3a\uff0cgradient vanishing \u95ee\u9898\u5bf9\u4e8e G \u5f88\u60b2\u60e8\uff0cG \u5f97\u4e0d\u5230\u6709\u6548\u7684\u5408\u7406\u7684\u4f20\u56de\u6765\u7684 gradient\uff0c\u5c31\u4f1a\u4e71\u5b66\uff0c\u4e71\u8dd1\uff0c\u6700\u540e\u5bfc\u81f4 collapse\u3002\u5927\u90e8\u5206\u8fc7\u53bb\u60f3\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u7684 GAN stablize\u7684\u5de5\u4f5c\u90fd\u662f\u4e3a\u4e86\u6539\u53d8\u8fd9\u4e2a value of gradient\uff0cgradient \u7684\u5927\u5c0f\u3002\u4f46\u662f\u8fd9\u4efd\u5de5\u4f5c[10]\uff0c\u6211\u4e2a\u4eba\u611f\u89c9\u662f\uff0c\u662f\u53e6\u8f9f\u8e4a\u5f84\uff0c\u4e3a\u4e86\u6539\u53d8\u4f18\u5316\u65b9\u5411\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5c31\u662f G \u4f1a\u8003\u8651\u9664\u4e86\u5f53\u524d\u4f18\u5316 step \u4ee5\u540e\u7684 k step \u4fe1\u606f\u3002\u5047\u5982\u6211\u8fd9\u4e48\u518d\u8d70 k \u6b65\uff0cdiscriminator \u4f1a\u7ed9\u6211\u4ec0\u4e48\u6837\u7684 feedback\u3002\u6240\u4ee5 G \u4f1a\u627e\u5230\uff0c\u5728 k \u6b65\u4ee5\u540e\uff0c\u6700\u80fd fool discriminator D \u7684\u201c\u65b9\u5411\u201d\u3002\u6240\u4ee5\uff0cG \u7684\u4f18\u5316\u76ee\u6807\uff0c\u5c31\u53d8\u6210\u4e86\u4ea4\u53c9\u4f18\u5316\uff08\u4ea4\u53c9\u4f18\u5316 G,D\uff0c\u8fd9\u4e5f\u662f GAN \u7684\u57fa\u672c\u65b9\u6cd5\uff09k \u6b65\u4ee5\u540e\u7684\u76ee\u6807\u3002\u8fd9\u6837\u6bcf\u6b21\u4f18\u5316 generator G\uff0c\u90fd\u662f\u8003\u8651\u4f18\u5316 k \u6b65\u4ee5\u540e\u7684\u60c5\u51b5\u3002<\/span><\/p>\n

\u4e0a\u9762\u8fd9\u4e9b\u5bf9\u4e8e GAN \u7684\u6539\u8fdb\u5de5\u4f5c\u90fd\u51e0\u4e4e\u662f\u5728\u77ed\u77ed\u4e00\u5e74\u534a\u65f6\u95f4\u5185\u5b8c\u6210\u7684\uff0c\u5c24\u5176\u662f\u8fd1\u534a\u5e74\u3002\u8fd9\u91cc\u9762\u6700\u5927\u7684\u539f\u56e0\u5c31\u5728\u4e8e GAN \u76f8\u8f83\u4e8e\u4ee5\u524d\u7684 generative models\uff0c\u5de7\u5999\u5730\u5c06\u201c\u771f\u5047\u201d\u6837\u672c\u8f6c\u6362\u4e3a\u4e00\u79cd\u9690\u6027\u7684 label\uff0c\u4ece\u800c\u5b9e\u73b0\u4e86\u4e00\u79cd\u201c\u65e0\u76d1\u7763\u201d\u7684\u751f\u6210\u5f0f\u6a21\u578b\u8bad\u7ec3\u6846\u67b6\u3002\u8fd9\u79cd\u601d\u60f3\u4e5f\u53ef\u4ee5\u4ece\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u770b\u505a word2vec \u4e2d Skip-Gram \u7684\u4e00\u79cd\u53d8\u5f62\u3002\u56e0\u6b64\uff0c\u5728\u8fd9\u6b21\u7684\u4ecb\u7ecd\u4e2d\uff0c\u6211\u4e5f\u540c\u6837\u52a0\u5165\u4e86\u5bf9\u4e8e word2vec \u6a21\u578b\u548c word embeddings \u76f8\u5173\u5e94\u7528\u7684\u4ecb\u7ecd\u3002\u610f\u60f3\u4e0d\u5230\u7684\u662f\uff0cword embeddings \u8fd9\u4e2a\u6982\u5ff5\uff0c\u4e0e\u6765\u53c2\u52a0\u7814\u8bfb\u8425\u7684\u5404\u4e2a\u9886\u57df\u7684\u8001\u5e08\u524d\u8f88\u4e0e\u540c\u5b66\u90fd\u606f\u606f\u76f8\u5173\u3002\u5927\u5bb6\u7eb7\u7eb7\u4ece\u5c42\u6b21\u5316\u6a21\u578b\u3001\u590d\u6742\u7f51\u7edc\u3001\u751f\u7269\u7f51\u7edc\u7b49\u89d2\u5ea6\uff0c\u63d0\u51fa\u4e86\u81ea\u5df1\u5bf9\u8fd9\u4e2a\u95ee\u9898\u7684\u7406\u89e3\u548c\u542f\u53d1\u3002\u6211\u4e5f\u4ece\u4e2d\u83b7\u76ca\u532a\u6d45\u3002\u8fd9\u4e5f\u8ba9\u6211\u770b\u5230\uff0c\u672a\u6765\uff0c\u4e0d\u4ec5\u4ec5\u662f GAN \u7684\u66f4\u591a\u6539\u8fdb\u503c\u5f97\u88ab\u671f\u5f85\uff0c\u65e0\u76d1\u7763\u5b66\u4e60\u548c\u751f\u6210\u5f0f\u6a21\u578b\u7684\u53d1\u5c55\u4e5f\u540c\u6837\u503c\u5f97\u5173\u6ce8\u3002\u8fd9\u79cd\u5173\u6ce8\uff0c\u4e0d\u4ec5\u4ec5\u662f\u6765\u81ea\u6211\u4eec\u5b66\u79d1\u7684\uff0c\u4e5f\u6765\u81ea\u4e8e\u6574\u4e2a\u79d1\u5b66\u9886\u57df\u3002<\/span><\/p>\n

\u9664\u4e86\u77e5\u8bc6\u7684\u6536\u83b7\uff0c\u8fd9\u6b21\u6d3b\u52a8\u7ed9\u6211\u6700\u5927\u7684\u611f\u53d7\u5c31\u662f\u5b66\u79d1\u95f4\u601d\u60f3\u7684\u78b0\u649e\u548c\u4e0e\u5fd7\u540c\u9053\u5408\u7684\u670b\u53cb\u4ea4\u6d41\u76f8\u5904\u7684\u6109\u60a6\u3002\u77ed\u77ed\u7684\u51e0\u5929\u65f6\u95f4\uff0c\u6211\u4eec\u51e0\u4e4e\u65e5\u4ee5\u7ee7\u591c\u5730\u8ba8\u8bba\u5b66\u672f\u95ee\u9898\uff0c\u767d\u5929\u5728\u5927\u4f1a\u8bae\u5ba4\uff0c\u665a\u4e0a\u5728\u5c0f\u5957\u623f\uff0c\u751a\u81f3\u996d\u684c\u4e0a\u3001\u8336\u9986\u91cc\uff0c\u90fd\u662f\u6211\u4eec\u7684\u8eab\u5f71\u548c\u8ba8\u8bba\u7684\u58f0\u97f3\u3002\u6bcf\u4e2a\u4eba\u53c2\u4f1a\u7684\u4eba\u7684\u8138\u4e0a\uff0c\u5c3d\u7ba1\u5076\u6709\u5026\u5bb9\uff0c\u4f46\u66f4\u591a\u5730\u90fd\u662f\u90a3\u79cd\u5bf9\u77e5\u8bc6\u7684\u6e34\u671b\u3001\u5bf9\u4e8e\u7b54\u6848\u7684\u6e34\u6c42\u3002\u6211\u4eec\u65e9\u5df2\u77e5\u9053\uff0c\u5373\u4f7f\u6211\u4eec\u4e0d\u80fd\u5f97\u5230\u51c6\u786e\u7684\u7b54\u6848\uff0c\u4f46\u63a2\u7d22\u7684\u8fc7\u7a0b\u672c\u8eab\u5c31\u662f\u4e00\u79cd\u5e78\u798f\uff0c\u66f4\u4f55\u51b5\uff0c\u6211\u4eec\u5728\u8fd9\u4e2a\u8270\u8f9b\u7684\u6c42\u7d22\u8fc7\u7a0b\u4e2d\uff0c\u7adf\u6709\u8fd9\u6837\u4e00\u7fa4\u5fd7\u540c\u9053\u5408\u7684\u670b\u53cb\u540c\u884c\u3002\u800c\u8fd9\u6837\u96be\u5f97\u7684\u4f53\u9a8c\uff0c\u5fc5\u987b\u611f\u8c22\u51ef\u98ce\u57fa\u91d1\u4f1a\u7ed9\u4e88\u7684\u5927\u529b\u652f\u6301\u548c\u5468\u5230\u7684\u5b89\u6392\u3002<\/span><\/p>\n

\u5982\u4eca\uff0c\u5199\u4e0b\u8fd9\u4e9b\u6587\u5b57\u65f6\uff0c\u6211\u4eec\u8fd9\u7fa4\u4eba\u5df2\u7ecf\u53c8\u56de\u5230\u5404\u81ea\u7684\u7814\u7a76\u5c97\u4f4d\u4e0a\u3002\u6211\u56de\u5230\u4e86\u9999\u6e2f\uff0c\u594b\u6218\u8bba\u6587\uff0c\u6211\u7684\u597d\u670b\u53cb\u6167\u73b2\uff0c\u594b\u6218\u5728\u5317\u4eac\u3002\u6211\u4eec\u4ecd\u7136\u4fdd\u6301\u7740\u4e24\u4e09\u5929\u4e00\u6b21\u7684\u5927\u5bc6\u5ea6\u5b66\u672f\u4ea4\u6d41\u3002\u6709\u4e9b\u4eba\u5219\u751a\u81f3\u56de\u5230\u4e86\u9065\u8fdc\u7684\u82f1\u56fd\u3002\u8fd9\u8ba9\u6211\u60f3\u8d77\u53e4\u4eba\u7684\u90a3\u53e5\u8bdd\uff0c\u541b\u5b50\u4e4b\u4ea4\u6de1\u5982\u6c34\u3002\u5b66\u672f\u4e0a\u7684\u670b\u53cb\u6709\u65f6\u5019\u4fbf\u662f\u5982\u6b64\uff0c\u541b\u5b50\u4e4b\u4ea4\uff0c\u6050\u6015\u4e5f\u662f\u6de1\u5982\u9ad8\u5c71\u6d41\u6c34\u3002\u4eba\u751f\u5728\u4e16\uff0c\u771f\u7406\u4e0e\u77e5\u5df1\u96be\u89c5\u3002\u7136\u800c\uff0c\u613f\u5bf9\u771f\u7406\u7684\u8ffd\u6c42\u548c\u5bf9\u77e5\u5df1\u7684\u73cd\u60dc\u80fd\u957f\u5b58\u3002\u82e5\u80fd\u5982\u6b64\uff0c\u8fd9\u6837\u4e0d\u5fc5\u6709\u5b66\u672f\u4ee5\u5916\u7684\u4eba\u60c5\u7275\u8fde\uff0c\u4e0d\u5fc5\u523b\u610f\u53bb\u82b1\u5fc3\u601d\u94bb\u8425\u7e41\u6742\u4eba\u9645\u3002\u6211\u4eec\u5404\u81ea\u542f\u7a0b\uff0c\u5e26\u7740\u5927\u5bb6\u66fe\u5728\u4e00\u8d77\u75af\u72c2\u8ba8\u8bba\u5b66\u672f\u7684\u7ecf\u5386\u4e0e\u611f\u53d7\uff0c\u5e26\u7740\u5bf9\u4e8e\u5404\u81ea\u9886\u57df\u66f4\u5e7f\u9614\u7684\u7406\u89e3\uff0c\u542f\u7a0b\u3002<\/span><\/p>\n

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\u201c\u6211\u7684\u80a9\u4e0a\u662f\u98ce\uff0c\u98ce\u4e0a\u662f\u95ea\u70c1\u7684\u661f\u7fa4\u3002\u201d\u2014\u2014\u5317\u5c9b<\/span><\/p>\n

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\u53c2\u8003\u8d44\u6599<\/span><\/p>\n<\/section>\n<\/section>\n

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1.\u300aGenerativeAdversarial Nets\u300b
<\/span><\/p>\n

2.\u300aConditionalGenerative Adversarial Nets\u300b<\/span><\/p>\n

3.\u300aDRAW:A Recurrent Neural Network For Image Generation\u300b<\/span><\/p>\n

4.\u300aDeepGenerative Image Models using a Laplacian Pyramid of Adversarial Networks\u300b<\/span><\/p>\n

5.\u300aGeneratingImages with Recurrent Adversarial Networks\u300b<\/span><\/p>\n

6.\u300aUnsupervisedRepresentation Learning with Deep ConvolutionalGenerative Adversarial Networks\u300b<\/span><\/p>\n

7.\u300aInfoGAN:Interpretable Representation Learning by Information Maximizing GenerativeAdversarial Nets\u300b<\/span><\/p>\n

8.\u300af-GAN:Training Generative Neural Samplers using Variational Divergence Minimization\u300b<\/span><\/p>\n

9.\u300aGenerativeMulti-Adversarial Networks\u300b<\/span><\/p>\n

10.\u300aUnrolledGenerative Adversarial Networks\u300b<\/span><\/p>\n

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

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\u7edf\u4e00\u4e4b\u8def\uff1a\u91cf\u5b50\u7ea0\u7f20\u3001\u65f6\u7a7a\u51e0\u4f55\u4e0e\u673a\u5668\u5b66\u4e60<\/a><\/span><\/p>\n

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\u5982\u4f55\u638c\u63e1\u5b87\u5b99\uff1f<\/span><\/a><\/h2>\n

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\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/strong><\/p>\n

\u82f1\u6587\u540d\uff1a Swarm Agents Club \uff0c<\/span>\u7b80\u79f0\u201c SA Club<\/span> \u201d\uff0c\u6210\u7acb\u4e8e 2008 \u5e74 \uff0c \u662f\u4e00\u4e2a\u4ece\u4e8b\u5b66\u672f\u7814\u7a76\u3001\u4eab\u53d7\u79d1\u5b66\u4e50\u8da3\u7684\u63a2\u7d22\u8005\u7684\u56e2\u4f53 \u3002\u5021\u5bfc\u4ee5\u5e73\u7b49\u5f00\u653e\u7684\u6001\u5ea6\u3001\u79d1\u5b66\u5b9e\u8bc1\u7684\u7cbe\u795e\uff0c\u8fdb\u884c\u8de8\u5b66\u79d1\u7684\u7814\u7a76\u4e0e\u4ea4\u6d41\uff0c\u529b\u56fe\u642d\u5efa\u4e00\u4e2a\u4e2d\u56fd\u7684 \u201c \u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240 \u201d\u3002<\/strong><\/span><\/span><\/p>\n

\u96c6\u667a\u4ff1\u4e50\u90e8\u7684\u53e3\u53f7\uff1a<\/span><\/p>\n

\u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01\uff01\uff01<\/p>\n

\u96c6\u667a\u4ff1\u4e50\u90e8\u7684\u4f7f\u547d\uff08\u5373\u76ee\u6807\uff09\uff1a<\/span><\/p>\n

\u8425\u9020\u81ea\u7531\u4ea4\u6d41\u5b66\u672f\u601d\u60f3\u7684\u5c0f\u751f\u5883\uff0c<\/p>\n

 \u5b55\u80b2\u5f00\u521b\u6027\u7684\u79d1\u5b66\u53d1\u73b0\u3002<\/p>\n

\"\u96c6\u667a\"<\/p>\n

\u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n

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\"\u96c6\u667a\"
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\u957f\u6309\u8bc6\u522b\u4e8c\u7ef4\u7801\uff0c\u5173\u6ce8\u96c6\u667aClub\uff0c<\/span><\/p>\n

\u8ba9\u6211\u4eec\u79bb\u79d1\u5b66\u63a2\u7d22\u66f4\u8fd1\u4e00\u6b65\u3002<\/span><\/p>\n<\/section>\n<\/section>\n

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\u59cb\u53d1\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\uff1a \u96c6\u667a\u4ff1\u4e50\u90e8 <\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

\u674e\u5ae3\u7136 \u9999\u6e2f\u7406\u5de5\u5927\u5b66\u5728\u8bfb\u535a\u58eb\u751f\uff0c\u7814\u7a76\u65b9\u5411\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u7684\u8bed\u4e49\u8868\u8fbe\u548c\u8bed\u8a00\u751f\u6210\uff0c\u81f4\u529b\u4e8e\u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u3001\u8ba4\u77e5\u79d1\u5b66\u7b49\u76f8\u5173\u9886\u57df\u7684\u65b9\u6cd5\u548c\u6210\u679c\u63a2\u7a76\u4eba\u7c7b\u8bed\u8a00\u4e60\u5f97\u548c\u8bed\u8a00\u8868\u8fbe\u7684\u5965\u79d8\u3002 2016\u5e7410\u67087\u65e5\u4e2d\u5348\uff0c\u6765\u81ea\u56db\u9762\u516b\u65b9\u7684\u4e00\u7fa4\u4eba\u9f50\u805a\u4e1c\u76f4\u95e8\u94f6\u5ea7\u3002\u6dc5\u6dc5\u6ca5\u6ca5\u7684\u79cb\u96e8\u5e76\u6ca1\u6709\u6d47\u606f\u8fd9\u7fa4\u79d1\u5b66\u7231\u597d\u8005\u7684\u70ed\u60c5\uff0c\u6bcf\u4e2a\u4eba\u90fd\u6ee1\u5fc3\u671f\u5f85\u7740\u5373\u5c06\u5f00\u542f\u7684…<\/p>\n","protected":false},"author":1,"featured_media":7985,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[23],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/7984"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7984"}],"version-history":[{"count":1,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/7984\/revisions"}],"predecessor-version":[{"id":8000,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/7984\/revisions\/8000"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/7985"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7984"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7984"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7984"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=7984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}