{"id":55982,"date":"2025-08-14T10:44:25","date_gmt":"2025-08-14T02:44:25","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/55982.html"},"modified":"2025-08-14T10:44:25","modified_gmt":"2025-08-14T02:44:25","slug":"%e6%8f%90%e5%8d%87cnn%e6%a8%a1%e5%9e%8b%e6%b3%9b%e5%8c%96%e8%83%bd%e5%8a%9b%e7%9a%84%e6%a0%b8%e5%bf%83%e6%96%b9%e6%b3%95%ef%bc%9a%e4%bb%8e%e6%95%b0%e6%8d%ae%e5%88%b0%e6%a8%a1%e5%9e%8b%e7%9a%84","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/55982.html","title":{"rendered":"\u63d0\u5347CNN\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u7684\u6838\u5fc3\u65b9\u6cd5\uff1a\u4ece\u6570\u636e\u5230\u6a21\u578b\u7684\u7cfb\u7edf\u4f18\u5316\u6307\u5357"},"content":{"rendered":"<p>\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d&#xff0c;\u6211\u4eec\u5e38\u5e38\u9047\u5230\u8fd9\u6837\u7684\u56f0\u5883&#xff1a;\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u51c6\u786e\u7387\u9ad8\u8fbe99%&#xff0c;\u4f46\u6d4b\u8bd5\u96c6\u4e0a\u5374\u66b4\u8dcc\u81f370%\u2014\u2014\u8fd9\u5c31\u662f\u5178\u578b\u7684\u300c\u8fc7\u62df\u5408\u300d&#xff0c;\u672c\u8d28\u662f\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u4e0d\u8db3\u3002\u6cdb\u5316\u80fd\u529b&#xff0c;\u5373\u6a21\u578b\u5bf9\u672a\u89c1\u8fc7\u6570\u636e\u7684\u9884\u6d4b\u80fd\u529b&#xff0c;\u662f\u8861\u91cf\u5176\u4ef7\u503c\u7684\u6838\u5fc3\u6307\u6807\u3002\u672c\u6587\u5c06\u4ece\u6570\u636e\u4f18\u5316\u3001\u6a21\u578b\u8bbe\u8ba1\u3001\u8bad\u7ec3\u7b56\u7565\u3001\u635f\u5931\u51fd\u6570\u56db\u5927\u7ef4\u5ea6&#xff0c;\u7cfb\u7edf\u603b\u7ed3\u63d0\u5347CNN\u6cdb\u5316\u80fd\u529b\u7684\u5b9e\u6218\u65b9\u6cd5&#xff0c;\u5e76\u7ed3\u5408\u5177\u4f53\u6848\u4f8b\u8bf4\u660e\u3002<\/p>\n<hr \/>\n<h3>\u4e00\u3001\u6570\u636e\u5c42\u9762&#xff1a;\u8ba9\u6a21\u578b\u300c\u89c1\u591a\u8bc6\u5e7f\u300d<\/h3>\n<p>\u6570\u636e\u662f\u6a21\u578b\u7684\u300c\u71c3\u6599\u300d&#xff0c;\u6570\u636e\u8d28\u91cf\u4e0e\u591a\u6837\u6027\u76f4\u63a5\u51b3\u5b9a\u4e86\u6a21\u578b\u7684\u4e0a\u9650\u3002\u63d0\u5347\u6cdb\u5316\u7684\u7b2c\u4e00\u6b65&#xff0c;\u662f\u4ece\u6570\u636e\u6e90\u5934\u89e3\u51b3\u95ee\u9898\u3002<\/p>\n<h4>1. 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\u6570\u636e\u6e05\u6d17\u4e0e\u5e73\u8861&#xff1a;\u6d88\u9664\u300c\u566a\u58f0\u300d\u4e0e\u300c\u504f\u89c1\u300d<\/h4>\n<ul>\n<li>\n<p>\u566a\u58f0\u5904\u7406&#xff1a;\u6807\u6ce8\u9519\u8bef\u662f\u5e38\u89c1\u566a\u58f0\u6e90&#xff08;\u5982\u628a\u300c\u732b\u300d\u6807\u6210\u300c\u72d7\u300d&#xff09;\u3002\u53ef\u901a\u8fc7\u7f6e\u4fe1\u5b66\u4e60&#xff08;Confident Learning&#xff09;\u81ea\u52a8\u8bc6\u522b\u5e76\u4fee\u6b63\u9519\u8bef\u6807\u7b7e&#xff0c;\u6216\u7528KNN\u7b97\u6cd5\u68c0\u6d4b\u79bb\u7fa4\u6837\u672c\u3002<\/p>\n<\/li>\n<li>\n<p>\u7c7b\u522b\u5e73\u8861&#xff1a;\u5f53\u67d0\u4e00\u7c7b\u6837\u672c\u5360\u6bd4\u8fc7\u9ad8&#xff08;\u59821:100\u7684\u7f55\u89c1\u75c5\u8bca\u65ad&#xff09;&#xff0c;\u6a21\u578b\u4f1a\u503e\u5411\u4e8e\u9884\u6d4b\u591a\u6570\u7c7b\u3002\u89e3\u51b3\u65b9\u6cd5\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u8fc7\u91c7\u6837&#xff08;\u590d\u5236\u5c11\u6570\u7c7b\u6837\u672c&#xff0c;\u6216\u7528SMOTE\u751f\u6210\u65b0\u6837\u672c&#xff09;&#xff1b;<\/li>\n<li>\u6b20\u91c7\u6837&#xff08;\u968f\u673a\u5220\u9664\u591a\u6570\u7c7b\u6837\u672c&#xff0c;\u4f46\u53ef\u80fd\u4e22\u5931\u4fe1\u606f&#xff09;&#xff1b;<\/li>\n<li>\u635f\u5931\u51fd\u6570\u52a0\u6743&#xff08;\u5982Focal Loss&#xff0c;\u5bf9\u96be\u6837\u672c\u8d4b\u4e88\u66f4\u9ad8\u6743\u91cd&#xff09;\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>3. \u6570\u636e\u5206\u5e03\u5bf9\u9f50&#xff1a;\u8ba9\u8bad\u7ec3\u96c6\u4e0e\u6d4b\u8bd5\u96c6\u300c\u540c\u6e90\u300d<\/h4>\n<p>\u82e5\u8bad\u7ec3\u96c6\u4e0e\u6d4b\u8bd5\u96c6\u5206\u5e03\u5dee\u5f02\u5927&#xff08;\u5982\u8bad\u7ec3\u96c6\u662f\u767d\u5929\u7684\u4eba\u8138&#xff0c;\u6d4b\u8bd5\u96c6\u662f\u591c\u665a\u7684&#xff09;&#xff0c;\u6a21\u578b\u5fc5\u7136\u5931\u6548\u3002\u6b64\u65f6\u9700&#xff1a;<\/p>\n<ul>\n<li>\u6536\u96c6\u66f4\u591a\u4e0e\u6d4b\u8bd5\u96c6\u540c\u5206\u5e03\u7684\u6570\u636e&#xff1b;<\/li>\n<li>\u4f7f\u7528**\u9886\u57df\u81ea\u9002\u5e94&#xff08;Domain Adaptation&#xff09;**\u6280\u672f&#xff0c;\u5982\u901a\u8fc7\u5bf9\u6297\u8bad\u7ec3&#xff08;DANN&#xff09;\u5bf9\u9f50\u6e90\u57df\u4e0e\u76ee\u6807\u57df\u7684\u7279\u5f81\u5206\u5e03&#xff1b;<\/li>\n<li>\u5bf9\u8f93\u5165\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316&#xff08;\u5982\u7528ImageNet\u7684\u5747\u503c[0.485, 0.456, 0.406]\u548c\u6807\u51c6\u5dee[0.229, 0.224, 0.225]\u6807\u51c6\u5316&#xff09;&#xff0c;\u786e\u4fdd\u8f93\u5165\u7a7a\u95f4\u4e00\u81f4\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e8c\u3001\u6a21\u578b\u8bbe\u8ba1&#xff1a;\u6784\u5efa\u300c\u7b80\u6d01\u800c\u5f3a\u5927\u300d\u7684\u7f51\u7edc<\/h3>\n<p>\u6a21\u578b\u7684\u590d\u6742\u5ea6\u4e0e\u6cdb\u5316\u80fd\u529b\u5448\u5012U\u578b\u5173\u7cfb\u2014\u2014\u8fc7\u4e8e\u7b80\u5355\u65e0\u6cd5\u6355\u6349\u7279\u5f81&#xff0c;\u8fc7\u4e8e\u590d\u6742\u5219\u5bb9\u6613\u8bb0\u4f4f\u566a\u58f0\u3002\u8bbe\u8ba1\u65f6\u9700\u5728\u300c\u5bb9\u91cf\u300d\u4e0e\u300c\u6b63\u5219\u5316\u300d\u95f4\u627e\u5e73\u8861\u3002<\/p>\n<h4>1. \u6b63\u5219\u5316\u6280\u672f&#xff1a;\u7ed9\u6a21\u578b\u52a0\u300c\u7ea6\u675f\u300d<\/h4>\n<ul>\n<li>\n<p>L2\u6b63\u5219\u5316&#xff08;\u6743\u91cd\u8870\u51cf&#xff09;&#xff1a;\u5728\u635f\u5931\u51fd\u6570\u4e2d\u6dfb\u52a0\u6743\u91cd\u53c2\u6570\u7684L2\u8303\u6570&#xff08;\u5982\u03bb*||W||\u00b2&#xff09;&#xff0c;\u8feb\u4f7f\u6a21\u578b\u504f\u597d\u5c0f\u6743\u91cd&#xff0c;\u907f\u514d\u67d0\u4e9b\u7279\u5f81\u88ab\u8fc7\u5ea6\u653e\u5927\u3002PyTorch\u4e2d\u53ef\u901a\u8fc7optimizer &#061; torch.optim.SGD(model.parameters(), lr&#061;0.01, weight_decay&#061;1e-4)\u5b9e\u73b0\u3002<\/p>\n<\/li>\n<li>\n<p>Dropout&#xff1a;\u8bad\u7ec3\u65f6\u968f\u673a\u300c\u5173\u95ed\u300d\u90e8\u5206\u795e\u7ecf\u5143&#xff08;\u5982\u901f\u7387\u8bbe\u4e3a0.5&#xff09;&#xff0c;\u5f3a\u5236\u6a21\u578b\u5b66\u4e60\u5197\u4f59\u7279\u5f81\u3002\u4f8b\u5982&#xff0c;ResNet\u5728\u6bcf\u4e2a\u5168\u8fde\u63a5\u5c42\u524d\u6dfb\u52a0Dropout(0.5)&#xff0c;\u53ef\u5c06ImageNet top-1\u51c6\u786e\u7387\u63d0\u53471-2%\u3002\u6ce8\u610f&#xff1a;Dropout\u4ec5\u5728\u8bad\u7ec3\u65f6\u542f\u7528&#xff0c;\u63a8\u7406\u65f6\u9700\u5173\u95ed&#xff08;\u6216\u4e58\u4ee5\u4fdd\u7559\u6982\u7387&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>Batch Normalization&#xff08;BN&#xff09;&#xff1a;\u5bf9\u6bcf\u5c42\u8f93\u5165\u8fdb\u884c\u5f52\u4e00\u5316&#xff08;\u5747\u503c0&#xff0c;\u65b9\u5dee1&#xff09;&#xff0c;\u5e76\u901a\u8fc7\u53ef\u5b66\u4e60\u7684\u03b3\u548c\u03b2\u8c03\u6574\u5206\u5e03\u3002BN\u4e0d\u4ec5\u80fd\u52a0\u901f\u8bad\u7ec3&#xff08;\u5141\u8bb8\u66f4\u5927\u5b66\u4e60\u7387&#xff09;&#xff0c;\u8fd8\u80fd\u901a\u8fc7\u300c\u566a\u58f0\u6ce8\u5165\u300d\u8d77\u5230\u8f7b\u5fae\u6b63\u5219\u4f5c\u7528\u3002\u5b9e\u9a8c\u8868\u660e&#xff0c;\u5728CIFAR-10\u4e0a&#xff0c;ResNet&#043;BN\u6bd4\u65e0BN\u7684\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u5f3a10%\u4ee5\u4e0a\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>2. \u63a7\u5236\u6a21\u578b\u590d\u6742\u5ea6&#xff1a;\u907f\u514d\u300c\u8fc7\u5ea6\u62df\u5408\u300d<\/h4>\n<ul>\n<li>\n<p>\u7b80\u5316\u7f51\u7edc\u7ed3\u6784&#xff1a;\u82e5\u6a21\u578b\u53c2\u6570\u91cf\u8fdc\u5927\u4e8e\u6837\u672c\u91cf&#xff08;\u5982\u7528ResNet-152\u8bad\u7ec31000\u5f20\u56fe\u7247&#xff09;&#xff0c;\u9700\u51cf\u5c11\u5c42\u6570\u6216\u901a\u9053\u6570\u3002\u4f8b\u5982&#xff0c;\u5bf9\u4e8e\u5c0f\u6570\u636e\u96c6&#xff08;&lt;10\u4e07\u5f20&#xff09;&#xff0c;MobileNetV3&#xff08;\u7ea65M\u53c2\u6570&#xff09;\u6bd4ResNet-50&#xff08;25M\u53c2\u6570&#xff09;\u66f4\u6613\u6cdb\u5316\u3002<\/p>\n<\/li>\n<li>\n<p>\u6b8b\u5dee\u8fde\u63a5&#xff08;Residual Connection&#xff09;&#xff1a;ResNet\u901a\u8fc7\u8df3\u8dc3\u8fde\u63a5&#xff08;Skip Connection&#xff09;\u7f13\u89e3\u6df1\u5c42\u7f51\u7edc\u7684\u68af\u5ea6\u6d88\u5931\u95ee\u9898&#xff0c;\u5141\u8bb8\u8bad\u7ec3\u66f4\u6df1\u7684\u7f51\u7edc\u800c\u4e0d\u6613\u8fc7\u62df\u5408\u3002\u5b9e\u9a8c\u8bc1\u660e&#xff0c;\u5f53\u7f51\u7edc\u6df1\u5ea6\u8d85\u8fc720\u5c42\u65f6&#xff0c;\u6b8b\u5dee\u7ed3\u6784\u7684\u6cdb\u5316\u8bef\u5dee\u6bd4\u666e\u901aCNN\u4f4e30%\u3002<\/p>\n<\/li>\n<li>\n<p>\u5206\u7ec4\u5377\u79ef\u4e0e\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef&#xff1a;\u5c06\u6807\u51c6\u5377\u79ef\u5206\u89e3\u4e3a\u5206\u7ec4\u8ba1\u7b97&#xff08;\u5982ResNeXt\u7684\u5206\u7ec4\u6570&#061;32&#xff09;\u6216\u9010\u901a\u9053&#043;\u9010\u70b9\u5377\u79ef&#xff08;\u5982MobileNet\u7684Depthwise Conv&#xff09;&#xff0c;\u51cf\u5c11\u53c2\u6570\u91cf\u7684\u540c\u65f6\u4fdd\u6301\u7279\u5f81\u63d0\u53d6\u80fd\u529b\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>3. \u96c6\u6210\u5b66\u4e60&#xff1a;\u7528\u300c\u7fa4\u4f53\u667a\u6167\u300d\u964d\u4f4e\u65b9\u5dee<\/h4>\n<ul>\n<li>\n<p>Bagging&#xff1a;\u8bad\u7ec3\u591a\u4e2a\u72ec\u7acb\u7684CNN\u6a21\u578b&#xff08;\u5982\u968f\u673a\u91c7\u6837\u4e0d\u540c\u7684\u6570\u636e\u5b50\u96c6&#xff09;&#xff0c;\u6700\u7ec8\u8f93\u51fa\u6295\u7968\u6216\u5e73\u5747\u7ed3\u679c\u3002\u4f8b\u5982&#xff0c;\u5728ImageNet\u4e0a&#xff0c;Bagging 10\u4e2aResNet-50\u6a21\u578b\u7684top-5\u9519\u8bef\u7387\u6bd4\u5355\u6a21\u578b\u4f4e1.2%\u3002<\/p>\n<\/li>\n<li>\n<p>\u6a21\u578b\u878d\u5408&#xff08;Ensemble&#xff09;&#xff1a;\u7ec4\u5408\u4e0d\u540c\u7ed3\u6784\u6216\u8d85\u53c2\u6570\u7684\u6a21\u578b&#xff08;\u5982CNN&#043;Transformer\u3001\u4e0d\u540c\u521d\u59cb\u5316\u7684ResNet&#xff09;&#xff0c;\u901a\u8fc7\u8f6f\u6295\u7968&#xff08;\u6982\u7387\u5e73\u5747&#xff09;\u63d0\u5347\u6cdb\u5316\u6027\u3002Kaggle\u7ade\u8d5b\u4e2d&#xff0c;Top 1\u65b9\u6848\u901a\u5e38\u4f7f\u7528\u6a21\u578b\u878d\u5408\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e09\u3001\u8bad\u7ec3\u7b56\u7565&#xff1a;\u8ba9\u6a21\u578b\u300c\u5b66\u4f1a\u6cdb\u5316\u300d<\/h3>\n<p>\u8bad\u7ec3\u8fc7\u7a0b\u7684\u7ec6\u8282&#xff08;\u5982\u5b66\u4e60\u7387\u8c03\u6574\u3001\u65e9\u505c&#xff09;\u76f4\u63a5\u5f71\u54cd\u6a21\u578b\u662f\u5426\u80fd\u6536\u655b\u5230\u300c\u6700\u4f18\u6cdb\u5316\u89e3\u300d\u3002<\/p>\n<h4>1. \u52a8\u6001\u5b66\u4e60\u7387\u8c03\u5ea6&#xff1a;\u907f\u514d\u300c\u9707\u8361\u300d\u4e0e\u300c\u505c\u6ede\u300d<\/h4>\n<ul>\n<li>\n<p>\u5b66\u4e60\u7387\u8870\u51cf&#xff08;LR Decay&#xff09;&#xff1a;\u521d\u59cb\u4f7f\u7528\u8f83\u5927\u5b66\u4e60\u7387&#xff08;\u59820.1&#xff09;\u5feb\u901f\u6536\u655b&#xff0c;\u540e\u671f\u964d\u4f4e\u5b66\u4e60\u7387&#xff08;\u5982\u6bcf30\u8f6e\u8870\u51cf0.1\u500d&#xff09;\u7cbe\u7ec6\u8c03\u4f18\u3002PyTorch\u7684torch.optim.lr_scheduler.StepLR\u53ef\u5b9e\u73b0\u6b64\u7b56\u7565\u3002<\/p>\n<\/li>\n<li>\n<p>\u4f59\u5f26\u9000\u706b&#xff08;Cosine Annealing&#xff09;&#xff1a;\u5b66\u4e60\u7387\u968f\u8bad\u7ec3\u8f6e\u6b21\u6309\u4f59\u5f26\u66f2\u7ebf\u4e0b\u964d&#xff0c;\u6700\u4f4e\u70b9\u63a5\u8fd10&#xff0c;\u5e2e\u52a9\u6a21\u578b\u8df3\u51fa\u5c40\u90e8\u6700\u4f18\u3002\u5b9e\u9a8c\u663e\u793a&#xff0c;\u5728CIFAR-10\u4e0a&#xff0c;\u4f59\u5f26\u9000\u706b\u8c03\u5ea6\u7684\u6a21\u578b\u6bd4\u56fa\u5b9a\u5b66\u4e60\u7387\u6a21\u578b\u51c6\u786e\u7387\u9ad82-3%\u3002<\/p>\n<\/li>\n<li>\n<p>\u81ea\u9002\u5e94\u5b66\u4e60\u7387&#xff1a;\u4f7f\u7528Adam\u3001RMSprop\u7b49\u4f18\u5316\u5668&#xff0c;\u6839\u636e\u68af\u5ea6\u81ea\u52a8\u8c03\u6574\u5b66\u4e60\u7387\u3002\u4f46\u9700\u6ce8\u610f&#xff1a;Adam\u5728\u5c0f\u6570\u636e\u96c6\u4e0a\u53ef\u80fd\u8fc7\u62df\u5408&#xff0c;\u6b64\u65f6SGD&#043;\u4f59\u5f26\u9000\u706b\u66f4\u7a33\u5b9a\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>2. \u65e9\u505c&#xff08;Early Stopping&#xff09;&#xff1a;\u53ca\u65f6\u300c\u5239\u8f66\u300d\u9632\u6b62\u8fc7\u62df\u5408<\/h4>\n<p>\u76d1\u63a7\u9a8c\u8bc1\u96c6\u635f\u5931&#xff0c;\u82e5\u8fde\u7eedN\u8f6e&#xff08;\u598210\u8f6e&#xff09;\u9a8c\u8bc1\u635f\u5931\u672a\u4e0b\u964d&#xff0c;\u5219\u63d0\u524d\u7ec8\u6b62\u8bad\u7ec3\u3002\u4f8b\u5982&#xff0c;\u5728\u8bad\u7ec3ResNet-34\u65f6&#xff0c;\u82e5\u7b2c50\u8f6e\u540e\u9a8c\u8bc1\u635f\u5931\u5f00\u59cb\u4e0a\u5347&#xff0c;\u800c\u8bad\u7ec3\u635f\u5931\u4ecd\u5728\u4e0b\u964d&#xff0c;\u8bf4\u660e\u6a21\u578b\u5f00\u59cb\u8bb0\u5fc6\u8bad\u7ec3\u6570\u636e&#xff0c;\u6b64\u65f6\u505c\u6b62\u53ef\u4fdd\u7559\u6700\u4f73\u6cdb\u5316\u72b6\u6001\u3002<\/p>\n<h4>3. \u591a\u9636\u6bb5\u8bad\u7ec3&#xff1a;\u4ece\u300c\u901a\u7528\u300d\u5230\u300c\u4e13\u7528\u300d<\/h4>\n<ul>\n<li>\n<p>\u9884\u8bad\u7ec3&#043;\u5fae\u8c03&#xff08;Fine-tuning&#xff09;&#xff1a;\u5728\u5927\u89c4\u6a21\u6570\u636e\u96c6&#xff08;\u5982ImageNet&#xff09;\u4e0a\u9884\u8bad\u7ec3\u57fa\u7840\u6a21\u578b&#xff08;\u83b7\u53d6\u901a\u7528\u89c6\u89c9\u7279\u5f81&#xff09;&#xff0c;\u518d\u5728\u76ee\u6807\u4efb\u52a1\u6570\u636e\u96c6\u4e0a\u5fae\u8c03\u6700\u540e\u51e0\u5c42\u3002\u4f8b\u5982&#xff0c;\u7528ImageNet\u9884\u8bad\u7ec3\u7684ResNet-50\u521d\u59cb\u5316&#xff0c;\u5728\u81ea\u5b9a\u4e49\u768410\u7c7b\u76ae\u80a4\u75c5\u6570\u636e\u96c6\u4e0a\u5fae\u8c03\u5168\u8fde\u63a5\u5c42&#xff0c;\u6bd4\u4ece\u5934\u8bad\u7ec3\u51c6\u786e\u7387\u9ad815%\u4ee5\u4e0a\u3002<\/p>\n<\/li>\n<li>\n<p>\u5206\u5c42\u5b66\u4e60\u7387&#xff1a;\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u5e95\u5c42&#xff08;\u5982\u5377\u79ef\u5c42&#xff09;\u63d0\u53d6\u901a\u7528\u7279\u5f81&#xff0c;\u5b66\u4e60\u7387\u8bbe\u4e3a\u8f83\u5c0f\u503c&#xff08;\u59821e-4&#xff09;&#xff1b;\u9876\u5c42&#xff08;\u5982\u5168\u8fde\u63a5\u5c42&#xff09;\u5b66\u4e60\u4efb\u52a1\u7279\u5b9a\u7279\u5f81&#xff0c;\u5b66\u4e60\u7387\u8bbe\u4e3a\u8f83\u5927\u503c&#xff08;\u59821e-3&#xff09;\u3002PyTorch\u4e2d\u53ef\u901a\u8fc7param_groups\u5b9e\u73b0\u5206\u5c42\u8bbe\u7f6e\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h3>\u56db\u3001\u635f\u5931\u51fd\u6570&#xff1a;\u5f15\u5bfc\u6a21\u578b\u300c\u5173\u6ce8\u6b63\u786e\u4fe1\u53f7\u300d<\/h3>\n<p>\u6807\u51c6\u4ea4\u53c9\u71b5\u635f\u5931\u53ef\u80fd\u65e0\u6cd5\u5e94\u5bf9\u590d\u6742\u573a\u666f&#xff08;\u5982\u7c7b\u522b\u4e0d\u5e73\u8861\u3001\u6a21\u7cca\u6837\u672c&#xff09;&#xff0c;\u9700\u6839\u636e\u4efb\u52a1\u7279\u6027\u8bbe\u8ba1\u635f\u5931\u51fd\u6570\u3002<\/p>\n<h4>1. \u89e3\u51b3\u7c7b\u522b\u4e0d\u5e73\u8861&#xff1a;Focal Loss<\/h4>\n<p>Focal Loss\u901a\u8fc7\u964d\u4f4e\u6613\u5206\u7c7b\u6837\u672c\u7684\u635f\u5931\u6743\u91cd&#xff08;\u5982\u8bbe\u7f6e\u03b1&#061;0.75&#xff0c;\u03b3&#061;2&#xff09;&#xff0c;\u8ba9\u6a21\u578b\u805a\u7126\u96be\u5206\u7c7b\u6837\u672c\u3002\u516c\u5f0f\u4e3a&#xff1a;<br \/>\n<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\">FL(pt)&#061;\u2212\u03b1(1\u2212pt)\u03b3log(pt)FL(p_t) &#061; -\u03b1(1-p_t)^\u03b3 log(p_t)<\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">F<\/span><span class=\"mord mathnormal\">L<\/span><span class=\"mopen\">(<\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2806em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">t<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\">\u2212<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0037em\">\u03b1<\/span><span class=\"mopen\">(<\/span><span class=\"mord\">1<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2806em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">t<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\"><span class=\"mclose\">)<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.6644em\"><span class=\"\" style=\"top: -3.063em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.0556em\">\u03b3<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0197em\">l<\/span><span class=\"mord mathnormal\">o<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0359em\">g<\/span><span class=\"mopen\">(<\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2806em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">t<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span><br \/>\n\u5176\u4e2d<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\">ptp_t<\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em;vertical-align: -0.1944em\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">p<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2806em\"><span class=\"\" style=\"top: -2.55em;margin-left: 0em;margin-right: 0.05em\"><span class=\"pstrut\" style=\"height: 2.7em\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">t<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em\"><span class=\"\"><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u662f\u6a21\u578b\u5bf9\u771f\u5b9e\u7c7b\u522b\u7684\u9884\u6d4b\u6982\u7387\u3002\u5728\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u4e2d&#xff0c;Focal Loss\u5c06RetinaNet\u7684mAP\u63d0\u5347\u4e8610%\u4ee5\u4e0a\u3002<\/p>\n<h4>2. \u63d0\u5347\u7279\u5f81\u5224\u522b\u6027&#xff1a;\u5bf9\u6bd4\u5b66\u4e60&#xff08;Contrastive Learning&#xff09;<\/h4>\n<p>\u901a\u8fc7\u6700\u5927\u5316\u6b63\u6837\u672c\u5bf9&#xff08;\u540c\u4e00\u56fe\u50cf\u7684\u4e0d\u540c\u589e\u5f3a\u7248\u672c&#xff09;\u7684\u76f8\u4f3c\u6027&#xff0c;\u6700\u5c0f\u5316\u8d1f\u6837\u672c\u5bf9&#xff08;\u4e0d\u540c\u56fe\u50cf&#xff09;\u7684\u76f8\u4f3c\u6027&#xff0c;\u4f7f\u6a21\u578b\u5b66\u4e60\u66f4\u5177\u5224\u522b\u6027\u7684\u7279\u5f81\u3002\u4f8b\u5982&#xff0c;SimCLR\u5728\u65e0\u6807\u7b7e\u6570\u636e\u4e0a\u9884\u8bad\u7ec3\u540e&#xff0c;\u8fc1\u79fb\u5230\u4e0b\u6e38\u4efb\u52a1&#xff08;\u5982\u5206\u7c7b&#xff09;\u7684\u6cdb\u5316\u80fd\u529b\u4f18\u4e8e\u76d1\u7763\u5b66\u4e60\u3002<\/p>\n<h4>3. \u9632\u6b62\u8fc7\u81ea\u4fe1&#xff1a;\u6807\u7b7e\u5e73\u6ed1&#xff08;Label Smoothing&#xff09;<\/h4>\n<p>\u5c06\u786c\u6807\u7b7e&#xff08;\u5982[1,0,0]&#xff09;\u8f6c\u4e3a\u8f6f\u6807\u7b7e&#xff08;\u5982[0.9,0.05,0.05]&#xff09;&#xff0c;\u907f\u514d\u6a21\u578b\u5bf9\u6b63\u786e\u7c7b\u522b\u8fc7\u5ea6\u81ea\u4fe1\u3002\u5b9e\u9a8c\u663e\u793a&#xff0c;\u5728CIFAR-10\u4e0a&#xff0c;\u6807\u7b7e\u5e73\u6ed1\u53ef\u5c06Top-1\u9519\u8bef\u7387\u4ece4.5%\u964d\u81f33.8%\u3002<\/p>\n<hr 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