{"id":3253,"date":"2025-07-20T10:03:55","date_gmt":"2025-07-20T02:03:55","guid":{"rendered":"https:\/\/www.gnn.club\/?p=3253"},"modified":"2025-07-20T10:03:55","modified_gmt":"2025-07-20T02:03:55","slug":"simam","status":"publish","type":"post","link":"http:\/\/gnn.club\/?p=3253","title":{"rendered":"SimAM"},"content":{"rendered":"<h1>\u57fa\u672c\u4fe1\u606f<\/h1>\n<ul>\n<li>\ud83d\udcf0\u6807\u9898: SimAM: A Simple, Parameter-Free Attention Module for  Convolutional Neural Networks<\/li>\n<li>\ud83d\udd8b\ufe0f\u4f5c\u8005: Lingxiao Yang<\/li>\n<li>\ud83c\udfdb\ufe0f\u673a\u6784: 1 Sun Yat-sen University (\u4e2d\u5c71\u5927\u5b66), 2 Key Laboratory of Machine Intelligence and Advanced Computing (\u673a\u5668\u667a\u80fd\u4e0e\u5148\u8fdb\u8ba1\u7b97\u6559\u80b2\u90e8\u91cd\u70b9\u5b9e\u9a8c\u5ba4), 3 Guangdong Provincial Key Laboratory of Big Data Analysis and Processing (\u5e7f\u4e1c\u7701\u5927\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406\u91cd\u70b9\u5b9e\u9a8c\u5ba4)<\/li>\n<li>\ud83d\udd17\u94fe\u63a5: -<\/li>\n<li>\ud83d\udd25\u5173\u952e\u8bcd: Attention Mechanism, Convolutional Neural Networks, Parameter-Free<\/li>\n<\/ul>\n<h2>\u6458\u8981\u6982\u8ff0<\/h2>\n<table border=\"1\" cellspacing=\"0\" cellpadding=\"5\">\n<tr>\n<th>\u9879\u76ee<\/th>\n<th>\u5185\u5bb9<\/th>\n<\/tr>\n<tr>\n<td>\ud83d\udcd6\u7814\u7a76\u80cc\u666f<\/td>\n<td>\u73b0\u6709\u6ce8\u610f\u529b\u6a21\u5757\u901a\u5e38\u4f9d\u8d56\u590d\u6742\u7ed3\u6784\u6216\u989d\u5916\u53c2\u6570\uff0c\u9650\u5236\u4e86\u6548\u7387\u548c\u6cdb\u5316\u80fd\u529b\u3002<\/td>\n<\/tr>\n<tr>\n<td>\ud83c\udfaf\u7814\u7a76\u76ee\u7684<\/td>\n<td>\u63d0\u51fa\u4e00\u79cd\u65e0\u9700\u53ef\u5b66\u4e60\u53c2\u6570\u7684\u7b80\u5355\u6ce8\u610f\u529b\u673a\u5236\uff08SimAM\uff09\uff0c\u63d0\u5347CNN\u6027\u80fd\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u270d\ufe0f\u7814\u7a76\u65b9\u6cd5<\/td>\n<td>\u57fa\u4e8e\u795e\u7ecf\u79d1\u5b66\u7406\u8bba\uff0c\u901a\u8fc7\u80fd\u91cf\u51fd\u6570\u63a8\u5bfc3D\u6ce8\u610f\u529b\u6743\u91cd\uff0c\u76f4\u63a5\u4f18\u5316\u7279\u5f81\u663e\u8457\u6027\u3002<\/td>\n<\/tr>\n<tr>\n<td>\ud83d\udd4a\ufe0f\u7814\u7a76\u5bf9\u8c61<\/td>\n<td>\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u7279\u5f81\u56fe\u3002<\/td>\n<\/tr>\n<tr>\n<td>\ud83d\udd0d\u7814\u7a76\u7ed3\u8bba<\/td>\n<td>SimAM\u5728\u591a\u4e2a\u57fa\u51c6\u4efb\u52a1\uff08\u5206\u7c7b\/\u68c0\u6d4b\/\u5206\u5272\uff09\u4e2d\u4f18\u4e8e\u73b0\u6709\u6ce8\u610f\u529b\u6a21\u5757\uff0c\u8ba1\u7b97\u5f00\u9500\u4f4e\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u2b50\u521b\u65b0\u70b9<\/td>\n<td>1) \u65e0\u53c2\u8bbe\u8ba1 2) \u7406\u8bba\u9a71\u52a8\u7684\u80fd\u91cf\u6700\u5c0f\u5316\u6846\u67b6 3) \u5373\u63d2\u5373\u7528\u4e14\u9ad8\u6548\u3002<\/td>\n<\/tr>\n<\/table>\n<h1>\u80cc\u666f<\/h1>\n<ul>\n<li>\n<p><strong>\u7814\u7a76\u80cc\u666f<\/strong>\uff1a<br \/>\n\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08ConvNets\uff09\u5728\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff08\u5982ImageNet\uff09\u4e0a\u7684\u8bad\u7ec3\u663e\u8457\u63d0\u5347\u4e86\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u7b49\u89c6\u89c9\u4efb\u52a1\u7684\u6027\u80fd\u3002\u73b0\u4ee3ConvNet\u901a\u5e38\u7531\u591a\u9636\u6bb5\u6a21\u5757\u5316\u7ed3\u6784\u7ec4\u6210\uff0c\u4f46\u8bbe\u8ba1\u9ad8\u6548\u6a21\u5757\u9700\u4f9d\u8d56\u4e13\u5bb6\u7ecf\u9a8c\u6216\u81ea\u52a8\u641c\u7d22\u7b56\u7565\uff0c\u6210\u672c\u9ad8\u6602\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8fc7\u53bb\u65b9\u6848<\/strong>\uff1a<\/p>\n<\/li>\n<\/ul>\n<ol>\n<li>\n<p><strong>\u6a21\u5757\u8bbe\u8ba1<\/strong>\uff1a\u4e3b\u6d41\u65b9\u6cd5\u5305\u62ec\u5806\u53e0\u5377\u79ef\u3001\u6b8b\u5dee\u8fde\u63a5\uff08ResNet\uff09\u548c\u5bc6\u96c6\u8fde\u63a5\uff08DenseNet\uff09\uff0c\u4f46\u9700\u590d\u6742\u4eba\u5de5\u8bbe\u8ba1\uff1b<\/p>\n<\/li>\n<li>\n<p><strong>\u6ce8\u610f\u529b\u673a\u5236<\/strong>\uff1a\u5982SE\u6a21\u5757\u901a\u8fc7\u901a\u9053\u6ce8\u610f\u529b\u589e\u5f3a\u7279\u5f81\uff0c\u4f46\u4ec5\u9650\u5355\u4e00\u7ef4\u5ea6\uff08\u901a\u9053\u6216\u7a7a\u95f4\uff09\uff0c\u4e14\u4f9d\u8d56\u542f\u53d1\u5f0f\u7ed3\u6784\uff08\u5982\u6c60\u5316\u64cd\u4f5c\uff09\uff0c\u7075\u6d3b\u6027\u4e0d\u8db3\uff1b<\/p>\n<\/li>\n<li>\n<p><strong>3D\u6743\u91cd\u751f\u6210<\/strong>\uff1a\u5df2\u6709\u5de5\u4f5c\u5c1d\u8bd5\u751f\u62103D\u6743\u91cd\uff0c\u4f46\u91c7\u7528\u624b\u5de5\u7f16\u7801\u5668-\u89e3\u7801\u5668\u7ed3\u6784\uff0c\u8ba1\u7b97\u6548\u7387\u4f4e\u3002<\/p>\n<\/li>\n<\/ol>\n<ul>\n<li><strong>\u7814\u7a76\u52a8\u673a<\/strong>\uff1a<br \/>\n\u4e3a\u89e3\u51b3\u73b0\u6709\u6ce8\u610f\u529b\u6a21\u5757\u7684\u5c40\u9650\u6027\uff08\u7ef4\u5ea6\u5355\u4e00\u3001\u7ed3\u6784\u590d\u6742\uff09\uff0c\u53d7\u795e\u7ecf\u79d1\u5b66\u542f\u53d1\uff0c\u63d0\u51fa\u4e00\u79cd\u57fa\u4e8e\u80fd\u91cf\u51fd\u6570\u7684\u65e0\u53c23D\u6ce8\u610f\u529b\u673a\u5236\uff08SimAM\uff09\uff0c\u76f4\u63a5\u4f18\u5316\u7279\u5f81\u663e\u8457\u6027\uff0c\u5b9e\u73b0\u9ad8\u6548\u5373\u63d2\u5373\u7528\u7684\u7279\u5f81\u589e\u5f3a\u3002<\/li>\n<\/ul>\n<h1>\u65b9\u6cd5<\/h1>\n<ul>\n<li>\n<p><strong>\u7406\u8bba\u80cc\u666f<\/strong>\uff1a<br \/>\nSimAM\u53d7\u54fa\u4e73\u52a8\u7269\u5927\u8111\u7684\u7a7a\u95f4\u6291\u5236\u73b0\u8c61\uff08spatial suppression\uff09\u542f\u53d1\uff0c\u5373\u663e\u8457\u795e\u7ecf\u5143\u4f1a\u6291\u5236\u5468\u56f4\u795e\u7ecf\u5143\u7684\u653e\u7535\u6d3b\u52a8\u3002\u8be5\u6a21\u5757\u901a\u8fc7\u91cf\u5316\u795e\u7ecf\u5143\u4e0e\u5176\u5468\u56f4\u795e\u7ecf\u5143\u7684\u7ebf\u6027\u53ef\u5206\u6027\uff08linear separability\uff09\u6765\u8bc4\u4f30\u5176\u91cd\u8981\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6280\u672f\u8def\u7ebf<\/strong>\uff1a<br \/>\n\u4e0d\u540c\u4e8e\u73b0\u6709\u65b9\u6cd5\uff08\u5982SE\u3001CBAM\uff09\u4ec5\u751f\u62101D\uff08\u901a\u9053\uff09\u62162D\uff08\u7a7a\u95f4\uff09\u6743\u91cd\uff0cSimAM\u76f4\u63a5\u63a8\u65ad3D\u6ce8\u610f\u529b\u6743\u91cd\uff08\u540c\u65f6\u8003\u8651\u901a\u9053\u548c\u7a7a\u95f4\u7ef4\u5ea6\uff09\uff0c\u66f4\u8d34\u5408\u4eba\u8111\u7684\u6ce8\u610f\u529b\u673a\u5236\uff08\u7279\u5f81\u4e0e\u7a7a\u95f4\u6ce8\u610f\u529b\u5171\u5b58\uff09<br \/>\n<img decoding=\"async\" src=\"https:\/\/gnnclub-1311496010.cos.ap-beijing.myqcloud.com\/wp-content\/uploads\/2025\/07\/20250716235518389.png\" alt=\"file\" \/><\/p>\n<\/li>\n<\/ul>\n<ol>\n<li><strong>\u80fd\u91cf\u51fd\u6570\u6784\u5efa<\/strong>\uff1a<br \/>\n$$<br \/>\ne_t\\left(w_t, b_t, y, x_i\\right)=\\left(y_t-\\hat{t}\\right)^2+\\frac{1}{M-1} \\sum_{i=1}^{M-1}\\left(y_o-\\hat{x}_i\\right)^2<br \/>\n$$<\/li>\n<\/ol>\n<ul>\n<li>\u76ee\u6807\uff1a\u8861\u91cf\u76ee\u6807\u795e\u7ecf\u5143 $t$ \u4e0e\u540c\u4e00\u901a\u9053\u5185\u5176\u4ed6\u795e\u7ecf\u5143 $x_i$ \u7684\u7ebf\u6027\u53ef\u5206\u6027\u3002<\/li>\n<li>\u53d8\u91cf\uff1a<br \/>\n$\\circ \\hat{t}=w_t t+b_t$ \u548c $\\hat{x}_i=w_t x_i+b_t$ \u662f\u5bf9 $t$ \u548c $x_i$ \u7684\u7ebf\u6027\u53d8\u6362\u3002<\/li>\n<li>$y_t$ \u548c $y_o$ \u662f\u4e8c\u5143\u6807\u7b7e\uff08\u59821\u548c\uff0d1\uff09\uff0c\u5206\u522b\u8868\u793a\u76ee\u6807\u795e\u7ecf\u5143\u4e0e\u5176\u4ed6\u795e\u7ecf\u5143\u7684\u7406\u60f3\u8f93\u51fa\u3002<\/li>\n<li>$M=H \\times W$ \u662f\u901a\u9053\u5185\u7684\u795e\u7ecf\u5143\u6570\u91cf\u3002<\/li>\n<li>\u610f\u4e49\uff1a\u6700\u5c0f\u5316\u8be5\u80fd\u91cf\u51fd\u6570\u65f6\uff0c\u76ee\u6807\u795e\u7ecf\u5143 $t$ \u7684\u8f93\u51fa\u8d8b\u8fd1\u4e8e $y_t$ \uff0c\u800c\u5176\u4ed6\u795e\u7ecf\u5143\u8d8b\u8fd1\u4e8e $y_o$ \uff0c\u4ece\u800c\u5b9e\u73b0\u533a\u5206\u3002<\/li>\n<\/ul>\n<ol start=\"2\">\n<li><strong>\u5f15\u5165\u6b63\u5219\u5316\u7684\u80fd\u91cf\u51fd\u6570<\/strong><\/li>\n<\/ol>\n<p>$$<br \/>\ne_t\\left(w_t, b_t, y, x_i\\right)=\\frac{1}{M-1} \\sum_{i=1}^{M-1}\\left(-1-\\left(w_t x_i+b_t\\right)\\right)^2+\\left(1-\\left(w_t t+b_t\\right)\\right)^2+\\lambda w_t^2<br \/>\n$$<\/p>\n<ul>\n<li>\u6539\u8fdb\uff1a<\/li>\n<li>\u8bbe\u5b9a $y_t=1$ \u548c $y_o=-1$ \uff0c\u660e\u786e\u533a\u5206\u76ee\u6807\u4e0e\u5468\u56f4\u795e\u7ecf\u5143\u3002<\/li>\n<li>\u52a0\u5165L2\u6b63\u5219\u9879 $\\lambda w_t^2$ \u9632\u6b62\u8fc7\u62df\u5408\u3002<\/li>\n<li>\u751f\u7269\u5b66\u4f9d\u636e\uff1a\u6a21\u62df\u7a7a\u95f4\u6291\u5236\u73b0\u8c61\uff0c\u5373\u663e\u8457\u795e\u7ecf\u5143\u4f1a\u6291\u5236\u5468\u56f4\u795e\u7ecf\u5143\u7684\u6fc0\u6d3b\u3002<\/li>\n<\/ul>\n<ol start=\"3\">\n<li><strong>\u95ed\u5f0f\u89e3<\/strong><\/li>\n<\/ol>\n<p>\u901a\u8fc7\u6700\u5c0f\u5316\u516c\u5f0f2\uff0c\u4f5c\u8005\u63a8\u5bfc\u51fa\u95ed\u5f0f\u89e3\uff1a<br \/>\n1\uff0e\u6743\u91cd $w_t$ \u7684\u89e3\uff1a$w_t=-\\frac{2\\left(t-\\mu_t\\right)}{\\left(t-\\mu_t\\right)^2+2 \\sigma_t^2+2 \\lambda}$<\/p>\n<ul>\n<li>$\\mu_t$ \u548c $\\sigma_t^2$ \u662f\u6392\u9664 $t$ \u540e\u5176\u4ed6\u795e\u7ecf\u5143\u7684\u5747\u503c\u548c\u65b9\u5dee\u3002<\/li>\n<li>\u5206\u6bcd\u4e2d\u7684 $\\lambda$ \u5e73\u8861\u4e86\u5c40\u90e8\u5dee\u5f02\u4e0e\u5168\u5c40\u7a33\u5b9a\u6027\u3002<\/li>\n<\/ul>\n<p>2\uff0e\u504f\u7f6e $b_t$ \u7684\u89e3\uff1a$b_t=-\\frac{1}{2}\\left(t+\\mu_t\\right) w_t$<br \/>\n\uff0d\u7531\u7ebf\u6027\u53d8\u6362\u7684\u5bf9\u79f0\u6027\u63a8\u5bfc\u800c\u6765\uff0c\u7b80\u5316\u8ba1\u7b97\u3002<\/p>\n<ol start=\"4\">\n<li><strong>\u6700\u5c0f\u80fd\u91cf\u7684\u8ba1\u7b97<\/strong><\/li>\n<\/ol>\n<p>$$<br \/>\ne_t^*=\\frac{4\\left(\\hat{\\sigma}^2+\\lambda\\right)}{(t-\\hat{\\mu})^2+2 \\hat{\\sigma}^2+2 \\lambda}<br \/>\n$$<\/p>\n<ul>\n<li>\u7b80\u5316\u5047\u8bbe\uff1a\u540c\u4e00\u901a\u9053\u5185\u6240\u6709\u795e\u7ecf\u5143\u5171\u4eab\u76f8\u540c\u7684\u5747\u503c $\\hat{\\mu}$ \u548c\u65b9\u5dee $\\hat{\\sigma}^2$\uff08\u57fa\u4e8eHariharan\u7b49\uff082012\uff09\uff3b1\uff3d\u7684\u5206\u5e03\u5047\u8bbe\uff09\u3002<\/li>\n<li>\u80fd\u91cf\u4e0e\u91cd\u8981\u6027\uff1a\u80fd\u91cf $e_t^<em>$ \u8d8a\u4f4e\uff0c\u795e\u7ecf\u5143 $t$ \u8d8a\u663e\u8457\uff08\u4e0e\u5468\u56f4\u5dee\u5f02\u5927\uff09\uff0c\u5176\u91cd\u8981\u6027 $1 \/ e_t^<\/em>$ \u8d8a\u9ad8\u3002<\/li>\n<\/ul>\n<ol start=\"5\">\n<li><strong>\u7279\u5f81\u7ec6\u5316<\/strong><\/li>\n<\/ol>\n<p>$$<br \/>\n\\tilde{X}=\\operatorname{sigmoid}\\left(\\frac{1}{E}\\right) \\odot X<br \/>\n$$<\/p>\n<ul>\n<li>\u64cd\u4f5c\uff1a\u901a\u8fc7Sigmoid\u51fd\u6570\u5c06\u80fd\u91cf\u5012\u6570\u8f6c\u6362\u4e3a\u6ce8\u610f\u529b\u6743\u91cd\uff0c\u6309\u5143\u7d20\u4e58\u4ee5\u539f\u59cb\u7279\u5f81 $X$ \u3002<\/li>\n<li>\u4f18\u52bf\uff1a\u65e0\u9700\u989d\u5916\u53c2\u6570\uff0c\u4ec5\u9700\u9010\u5143\u7d20\u8ba1\u7b97\uff0c\u9ad8\u6548\u4e14\u6613\u4e8e\u5b9e\u73b0\uff08\u5982\u56fe3\u7684PyTorch\u4ee3\u7801\u6240\u793a\uff09\u3002<\/li>\n<\/ul>\n<h1>\u7ed3\u8bba<\/h1>\n<ul>\n<li>\n<p>\u672c\u7814\u7a76\u57fa\u4e8e\u54fa\u4e73\u52a8\u7269\u5927\u8111\u795e\u7ecf\u79d1\u5b66\u7406\u8bba\uff08\u7a7a\u95f4\u6291\u5236\u7406\u8bba\uff09\uff0c\u63d0\u51fa\u65e0\u53c2\u6570\u6ce8\u610f\u529b\u6a21\u5757SimAM\uff0c\u4e3a\u89c6\u89c9\u4efb\u52a1\u4e2d\u7684\u7279\u5f81\u4f18\u5316\u63d0\u4f9b\u4e86\u7406\u8bba\u9a71\u52a8\u7684\u65b0\u8303\u5f0f\uff0c\u514b\u670d\u4e86\u4f20\u7edf\u6ce8\u610f\u529b\u673a\u5236\u4f9d\u8d56\u542f\u53d1\u5f0f\u8bbe\u8ba1\u7684\u5c40\u9650\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4f18\u70b9<\/strong>\uff1a<br \/>\n1) \u7406\u8bba\u89e3\u91ca\u6027\u5f3a\uff08\u80fd\u91cf\u51fd\u6570\u5efa\u6a21\u795e\u7ecf\u5143\u663e\u8457\u6027\uff09\uff1b<br \/>\n2) \u8ba1\u7b97\u9ad8\u6548\uff08\u95ed\u5f0f\u89e3\u907f\u514d\u8fed\u4ee3\uff09\uff1b<br \/>\n3) 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