{"id":39263,"date":"2018-04-21T00:00:12","date_gmt":"2018-04-20T16:00:12","guid":{"rendered":"https:\/\/case.ntu.edu.tw\/blog\/?p=39263"},"modified":"2022-01-05T02:12:03","modified_gmt":"2022-01-04T18:12:03","slug":"%e5%be%9e%e7%a5%9e%e7%b6%93%e5%85%83%e9%96%8b%e5%a7%8b%e7%9a%84%e4%ba%ba%e5%b7%a5%e6%99%ba%e6%85%a7-2","status":"publish","type":"post","link":"https:\/\/case.ntu.edu.tw\/blog\/?p=39263","title":{"rendered":"\u5f9e\u795e\u7d93\u5143\u958b\u59cb\u7684\u4eba\u5de5\u667a\u6167"},"content":{"rendered":"
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\"\u5f9e\u795e\u7d93\u5143\u958b\u59cb\u7684\u4eba\u5de5\u667a\u6167\"<\/div>\n<\/div>\n
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\u53f2\u4e39\u4f5b\u5927\u5b78\u61c9\u7528\u7269\u7406\u7cfb\u535a\u58eb\u73ed \u9673\u5955\u5ef7<\/strong><\/p>\n

\u73fe\u5728\u8a31\u591a\u4eba\u9ad4\u5668\u5b98\u90fd\u80fd\u88ab\u53d6\u4ee3\uff0c\u4f8b\u5982\u8449\u514b\u819c\u3001\u4eba\u9020\u8072\u5e36\u548c\u7fa9\u80a2\u7b49\u3002\u79d1\u5e7b\u5c0f\u8aaa\u4e2d\uff0c\u4f86\u81ea\u4e0d\u540c\u5c4d\u9ad4\u7684\u5668\u5b98\u88ab\u62fc\u6e4a\u6210\u79d1\u5b78\u602a\u4eba\uff0c\u5982\u4eca\u6211\u5011\u5e7e\u4e4e\u80fd\u7528\u4eba\u5de5\u5668\u5b98\u9054\u6210\u4e86\u3002\u4f46\u662f\u8981\u9054\u5230\u300c\u4eba\u9020\u4eba\u300d\uff0c\u50c5\u50c5\u5c07\u4eba\u9020\u5668\u5b98\u62fc\u6e4a\u8d77\u4f86\u662f\u4e0d\u5920\u7684\u3002\u9664\u4e86\u6a5f\u68b0\u6027\u7684\u5668\u5b98\u529f\u80fd\uff0c\u795e\u7d93\u5143\u5c07\u8cc7\u8a0a\u5f9e\u5404\u500b\u5668\u5b98\u5f59\u6574\u5230\u4e2d\u6a1e\uff0c\u5f62\u6210\u4fd7\u7a31\u7684\u610f\u8b58\u3002<\/span><\/p>\n

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\"\"<\/a><\/p>\n

\u5716\u4e00\u3001(A) \u795e\u7d93\u5143\u7684\u751f\u7269\u69cb\u9020\u3002(B)\u4eba\u5de5\u795e\u7d93\u5143\u7684\u6a21\u578b\u3002(C)\u4e00\u4e9b\u795e\u7d93\u7db2\u8def\u4e0d\u540c\u9375\u7d50\u65b9\u5f0f\u7684\u4f8b\u5b50\u3002\uff08\u5716\u7247\u4f86\u6e90\uff1a\u53c3\u8003\u8cc7\u65991\uff09<\/p>\n<\/div>\n

\u6df1\u5ea6\u5b78\u7fd2\uff01\u6e1b\u5c11\u8b8a\u6578\uff01<\/strong><\/p>\n

\u795e\u7d93\u7db2\u8def\u6709\u8a31\u591a\u4e0d\u540c\u7684\u9375\u7d50\u65b9\u5f0f(\u5716\u4e00C)\u3002\u8981\u300c\u5728\u61c9\u7528\u554f\u984c\u4e2d\u8868\u73fe\u6700\u597d\u300d\uff0c\u5fc5\u9808\u5f9e\u6240\u6709\u53ef\u80fd\u7684\u795e\u7d93\u5143\u9375\u7d50\u65b9\u5f0f\u4e2d\u6311\u51fa\u4e00\u500b\u6700\u597d\u7684\u3002\u5982\u679c\u4e00\u500b\u7db2\u8def\u6709N\u500b\u795e\u7d93\u5143\uff0c\u5b83\u5011\u4e4b\u9593\u5171\u6709\u7d04N\u5e73\u65b9\u500b\u9375\u7d50\u3002\u4ee5\u4e00\u5f35256*256\u50cf\u7d20\u7684\u5f71\u50cf\u70ba\u4f8b(\u60f3\u50cf\u6bcf\u4e00\u500b\u50cf\u7d20\u90fd\u662f\u4e00\u500b\u8996\u795e\u7d93\u5143)\uff0c\u5171\u6709\u8d85\u904e6\u842c\u500b\u795e\u7d93\u5143\u548c40\u5104\u500b\u9375\u7d50\u3002\u8981\u5f9e\u8d85\u904e40\u5104\u500b\u8b8a\u6578\u4e2d\u6311\u51fa\u4e00\u500b\u8868\u73fe\u6700\u597d\u7684\u7d44\u5408\u662f\u4e00\u500b\u56f0\u96e3\u7684\u554f\u984c\u3002<\/p>\n

\u4e00\u7a2e\u7c21\u5316\u7684\u65b9\u5f0f\u662f\u9650\u5236\u795e\u7d93\u5143\u7684\u9375\u7d50\u3002\u79d1\u5b78\u5bb6\u628a\u795e\u7d93\u5143\u5206\u621010\u5c64\uff0c\u5c64\u5c64\u6392\u5217\u3002\u55ae\u5c64\u5167\u7684\u795e\u7d93\u5143\u6c92\u6709\u9375\u7d50\uff0c\u53ea\u6709\u76f8\u9130\u5c64\u6709\u9375\u7d50\u3002\u5982\u6b64\u4e00\u4f86\u7e3d\u5171\u53ea\u5269\u4e0b\u7d041\/10\u7684\u8b8a\u6578\uff0c\u8a08\u7b97\u901f\u5ea6\u63d0\u534710\u500d\uff01\u9019\u6a23\u7684\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u901a\u5e38\u88ab\u7a31\u70ba\u300c\u6df1\u5ea6\u5b78\u7fd2(Deep Learning)\u300d\u3002\u300c\u6df1\u5ea6\u300d\u4ee3\u8868\u795e\u7d93\u5143\u88ab\u5206\u6210\u5f88\u591a\u5c64\uff0c\u8d8a\u6df1\u4ee3\u8868\u5c64\u6578\u8d8a\u591a\u3002\u800c\u300c\u5b78\u7fd2\u300d\u6307\u7684\u662f\u5f9e\u773e\u591a\u8b8a\u6578\u6311\u51fa\u4e00\u500b\u6700\u4f73\u89e3\u7684\u6578\u5b78\u904e\u7a0b\u3002<\/p>\n

\"\"<\/a><\/p>\n

\u5716\u4e8c\u3001(A)\u4e00\u822c\u7684\u6df1\u5ea6\u5b78\u7fd2\u67b6\u69cb\u793a\u610f\u5716\u3002(B)\u7121\u8ad6\u8c93\u662f\u5728\u5f71\u50cf\u7684\u5de6\u4e0a\u89d2\u9084\u662f\u53f3\u4e0b\u89d2\u90fd\u8981\u80fd\u88ab\u8fa8\u8b58\u51fa\u4f86\u3002\u9019\u500b\u7a7a\u9593\u95dc\u806f\u6027\u70ba\u795e\u7d93\u7db2\u8def\u52a0\u4e0a\u4e86\u4e00\u500b\u9650\u5236\uff0c\u4f7f\u7368\u7acb\u8b8a\u6578\u66f4\u5c11\u3002(C)\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u793a\u610f\u5716\u3002<\/p>\n<\/div>\n

\u5728\u96fb\u8166\u8996\u89ba\u4e2d\u589e\u52a0\u6df1\u5ea6 \u2013 \u5377\u7a4d\u795e\u7d93\u7db2\u8def<\/strong><\/p>\n

\u6df1\u5ea6\u5b78\u7fd2\u901a\u5e38\u8d8a\u6df1\u6548\u679c\u8d8a\u597d\u3002\u5716\u4e8cA\u662f\u4e00\u500b\u6df1\u5ea6\u5b78\u7fd2\u7684\u795e\u7d93\u7db2\u8def\uff0c\u6bcf\u4e00\u689d\u7dda\u90fd\u662f\u4e00\u500b\u795e\u7d93\u5143\u9593\u7684\u9375\u7d50\uff0c\u5b83\u6c7a\u5b9a\u8cc7\u8a0a\u5f9e\u4e00\u500b\u795e\u7d93\u5143\u6d41\u5230\u53e6\u4e00\u500b\u795e\u7d93\u5143\u7684\u5f37\u5ea6\uff0c\u662f\u4e00\u500b\u8b8a\u6578\u3002\u5728\u96fb\u8166\u8996\u89ba\u4e2d\uff0c\u4eba\u5de5\u667a\u6167\u5f9e\u5f71\u50cf\u4e2d\u8fa8\u8b58\u51fa\u7269\u9ad4\u3002\u4ee5\u5716\u4e8cB\u70ba\u4f8b\uff0c\u8c93\u7684\u5728\u5f71\u50cf\u4e2d\u4efb\u4f55\u4f4d\u7f6e\u4eba\u5de5\u667a\u6167\u90fd\u8981\u80fd\u8fa8\u8b58\u51fa\u4f86\u3002\u56e0\u6b64\u795e\u7d93\u5143\u9023\u7d50\u65b9\u5f0f\u4e0d\u61c9\u8a72\u96a8\u7a7a\u9593\u8b8a\u5316\uff0c\u4e5f\u5c31\u662f\u8aaa\u5169\u5f35\u5f71\u50cf\u4e2d\u7684\u6a58\u8272\u6846\u6846\u9644\u8fd1\u7684\u795e\u7d93\u5143\u9023\u7d50\u65b9\u5f0f\u61c9\u8a72\u8981\u76f8\u540c\uff0c\u800c\u4e0d\u662f\u7368\u7acb\u7684\u8b8a\u6578\u3002\u5229\u7528\u9019\u7a2e\u7a7a\u9593\u95dc\u806f\u6027\u628a\u5c64\u8207\u5c64\u4e4b\u9593\u7684\u8b8a\u6578\u6e1b\u5c11\uff0c\u5c31\u80fd\u5728\u7e3d\u8b8a\u6578\u6578\u76ee\u4e0d\u589e\u52a0\u592a\u591a\u7684\u60c5\u6cc1\u4e0b\u589e\u52a0\u6df1\u5ea6\uff0c\u4f7f\u795e\u7d93\u7db2\u8def\u6548\u679c\u8b8a\u5f97\u66f4\u597d\u3002\u56e0\u70ba\u5b83\u7684\u6578\u5b78\u7d30\u7bc0\uff0c\u9019\u7a2e\u67b6\u69cb\u53eb\u505a\u300c\u5377\u7a4d\u795e\u7d93\u7db2\u8def(Convolutional Neural Network, CNN)\u300d\u3002\u4e00\u500b\u77e5\u540d\u7684\u4f8b\u5b50\u662f\u82f1\u570b\u725b\u6d25\u5927\u5b78\u7684\u8996\u89ba\u5e7e\u4f55\u5718\u968a(Visual Geometry Group, VGG)\uff0c\u7528\u6df1\u5ea6\u70ba16\u5c64\u7684\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u8d0f\u5f972014\u5e74\u7684\u5f71\u50cf\u8fa8\u8b58\u5927\u8cfdImageNet\u7af6\u8cfd\u9805\u76ee[2]\u3002<\/p>\n

\u7a0d\u5fae\u89e3\u653e\uff0c\u6548\u679c\u66f4\u597d \u2013 \u6b98\u5dee\u795e\u7d93\u7db2\u8def\u548c\u7dca\u5bc6\u795e\u7d93\u7db2\u8def<\/strong><\/p>\n

\u6301\u7e8c\u52a0\u6df1\u795e\u7d93\u7db2\u8def\u4e4b\u5f8c\uff0c\u6548\u679c\u4e26\u6c92\u6709\u50cf\u7406\u8ad6\u9810\u6e2c\u4e00\u6a23\u6301\u7e8c\u589e\u52a0\u3002\u53ef\u80fd\u7684\u539f\u56e0\u662f\u8cc7\u8a0a\u5728\u8f38\u5165\u5c64\u548c\u8f38\u51fa\u5c64\u4e4b\u9593\u7684\u5c64\u5c64\u7db2\u8def\u4e2d\u6d41\u5931\u4e86\u3002\u70ba\u4e86\u89e3\u6c7a\u9019\u500b\u554f\u984c\uff0c\u79d1\u5b78\u5bb6\u89e3\u653e\u300c\u795e\u7d93\u5143\u53ea\u548c\u76f8\u9130\u5c64\u9375\u7d50\u300d\u9019\u500b\u9650\u5236\u3002\u5982\u5716\u4e09A\uff0c\u9664\u4e86\u76f8\u9130\u7684\u795e\u7d93\u5c64\u4e4b\u9593\u7684\u9375\u7d50\uff0c\u795e\u7d93\u7db2\u8def\u5c641\u4e5f\u8de8\u8d8a\u5169\u5c64\u7db2\u8def\u5c64\uff0c\u548c\u7db2\u8def\u5c644\u9375\u7d50(\u5716\u4e09A\u7d05\u8272\u7bad\u982d)\u3002\u9019\u6a23\u7684\u67b6\u69cb\u53eb\u505a\u300c\u6df1\u5ea6\u6b98\u5dee\u7db2\u8def(Deep Residual Network)\u300d\uff0c\u6df1\u5ea6\u53ef\u4ee5\u9054\u5230150\u5c64\u4ee5\u4e0a\uff0c\u6548\u679c\u6bd4\u725b\u6d25\u5927\u5b78\u7684VGG\u5718\u968a\u66f4\u597d\uff0c\u8d0f\u5f97\u4e862015\u5e74ImageNet\u7af6\u8cfd\u9805\u76ee [3]\u3002\u4e0d\u4e45\u4e4b\u5f8c\uff0c\u6df1\u5ea6\u7db2\u8def\u88ab\u66f4\u9032\u4e00\u6b65\u5730\u89e3\u653e\u3002\u5982\u5716\u4e09B\uff0c\u795e\u7d93\u7db2\u8def\u5c64\u4e0d\u50c5\u50c5\u662f\u8de8\u8d8a\u5169\u5c64\u76f8\u9023\uff0c\u800c\u662f\u9023\u7e8c\u4e94\u5c64\u6bcf\u4e00\u5c64\u90fd\u76f8\u9023\u3002\u9019\u6a23\u7684\u67b6\u69cb\u53eb\u505a\u300c\u6df1\u5ea6\u7dca\u5bc6\u7db2\u8def(Densely Connected Network)\u300d\uff0c\u6548\u679c\u66f4\u597d[4]\u3002\u9019\u500b\u65b9\u6cd5\u57282017\u5e74\u7372\u5f97\u9802\u5c16\u96fb\u8166\u8996\u89ba\u6703\u8b70CVPR\u7684\u6700\u4f73\u8ad6\u6587\u734e\u3002\u4eba\u5de5\u795e\u7d93\u7db2\u8def\u662f\u7576\u4eca\u6700\u71b1\u9580\u7684\u4eba\u5de5\u667a\u6167\u9818\u57df\uff0c\u4eca\u5f8c\u9084\u6703\u6301\u7e8c\u51fa\u73fe\u5404\u5f0f\u5404\u6a23\u3001\u8868\u73fe\u66f4\u597d\u7684\u795e\u7d93\u7db2\u8def\u3002<\/p>\n

\"\"<\/a><\/p>\n

\u5716\u4e09\u3001(A) \u6df1\u5ea6\u6b98\u5dee\u7db2\u8def\u793a\u610f\u5716[\u53c33]\u3002(B)\u6df1\u5ea6\u7dca\u5bc6\u7db2\u8def\u793a\u610f\u5716\u3002\uff08\u5716\u7247\u4f86\u6e90\uff1a\u53c3\u8003\u8cc7\u65994\uff09<\/p>\n<\/div>\n

\n

\u53c3\u8003\u6587\u737b<\/strong><\/p>\n

    \n
  • [1] The Asimov Institute, The neural network zoo (2016)<\/li>\n
  • [2] Karen Simonyan et al., Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 (2015)<\/li>\n
  • [3] Kaiming He et al., Deep Residual Learning for Image Recognition, arXiv:1512.03385 (2015)<\/li>\n
  • [4] Gao Huang et al., Densely Connected Convolutional Networks, CVPR (2017)<\/li>\n<\/ul>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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