{"id":4677,"date":"2022-07-20T19:36:15","date_gmt":"2022-07-20T11:36:15","guid":{"rendered":"http:\/\/139.9.1.231\/?p=4677"},"modified":"2022-08-28T15:38:59","modified_gmt":"2022-08-28T07:38:59","slug":"deniose","status":"publish","type":"post","link":"http:\/\/139.9.1.231\/index.php\/2022\/07\/20\/deniose\/","title":{"rendered":"\u4e2d\u5174\u5927\u8d5b"},"content":{"rendered":"\n<p>\u4e2d\u5174\u6367\u6708\u5927\u8d5b \uff1ahttps:\/\/zte.hina.com\/zte\/index<\/p>\n\n\n\n<p>\u56fe\u50cf\u53bb\u566a\u8d5b\u9898\u80cc\u666f\u56fe\u50cf\u53bb\u566a\u662f\u673a\u5668\u89c6\u89c9\u9886\u57df\u91cd\u8981\u4efb\u52a1\uff0c\u56fe\u50cf\u53bb\u566a\u6a21\u5757\u5728\u5b89\u9632\uff0c\u81ea\u52a8\u9a7e\u9a76\uff0c\u4f20\u611f\uff0c\u533b\u5b66\u5f71\u50cf\uff0c\u6d88\u8d39\u7535\u5b50\u7b49\u9886\u57df\u90fd\u662f\u91cd\u8981\u7684\u524d\u7aef\u56fe\u50cf\u5904\u7406\u6a21\u5757\u3002\u6d88\u8d39\u7ea7\u7535\u5b50\u4ea7\u54c1(\u4f8b\u5982\u624b\u673a)\u51fa\u4e8e\u6210\u672c\u8003\u8651\uff0c\u5728\u4f4e\u7167\u5ea6\u548c\u9ad8ISO\u6761\u4ef6\u4e0b\uff0c\u566a\u58f0\u5bf9\u6210\u50cf\u8d28\u91cf\u7684\u964d\u7ea7\u66f4\u52a0\u4e25\u91cd\u3002\u5bf9\u4e8e\u4f20\u7edf\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\uff0c\u5e38\u89c1\u53bb\u566a\u7b97\u6cd5\u5305\u542b\u53cc\u8fb9(bilateral)\u6ee4\u6ce2\uff0cNLM (non local mean)\u6ee4\u6ce2\uff0cBM3D\uff0c\u591a\u5e27(3D)\u964d\u566a\u65b9\u6848\u7b49\u591a\u79cd\u65b9\u6848\uff0c\u4ea7\u54c1\u5b9e\u73b0\u4e0a\u9700\u8981\u517c\u987e\u6027\u80fd\u548c\u590d\u6742\u5ea6\u3002<br>AI\u53ef\u8fdb\u4e00\u6b65\u63d0\u5347\u56fe\u50cf\u4e3b\u5ba2\u89c2\u8d28\u91cf\u5728\u5b66\u672f\u548c\u5de5\u4e1a\u754c\u5f97\u5230\u4e86\u5e7f\u6cdb\u8ba4\u8bc1\u3002\u5bf9\u4e8e\u624b\u673a\u4ea7\u54c1\uff0cAI\u6b63\u5feb\u901f\u8865\u5145\u548c\u66ff\u4ee3\u4f20\u7edf\u624b\u673aISP(Image signal processing)\u4e2d\u7684\u75db\u70b9\u96be\u70b9\uff0c\u4f8b\u5982\u53ef\u8fdb\u884cAI-based\u53bb\u566a\uff0c\u52a8\u6001\u8303\u56f4\u589e\u5f3a\uff0c\u8d85\u5206\u8fa8\uff0c\u8d85\u7ea7\u591c\u666f\uff0c\u751a\u81f3AI ISP\u7b49\u3002<\/p>\n\n\n\n<p>\u63d0\u4ea4\u8bf4\u660e<\/p>\n\n\n\n<p>1. \u53c2\u8d5b\u8005\u9700\u8981\u6839\u636e\u4e3e\u529e\u65b9\u63d0\u4f9b\u768410\u5f20noisy\u56fe\u7247\u63d0\u4ea4\u76f8\u5e9410\u5f20denoise\u56fe\u7247\u5b58\u653e\u81f3\u6587\u4ef6\u5939\u201cdata\u201d\u4e0b\uff0c\u547d\u540d\u65b9\u5f0f\u4e3adenoise0.dng\u81f3denoise9.dng\uff0c\u6ce8\u610f\u4e0a\u4f20denoise RAW\u56fe\u503c\u57df\u4e3a[black_level, white_level] = [1024,16383]\uff0c\u53ef\u53c2\u7167baseline\u4ee3\u7801\uff1b<br>2. \u53c2\u8d5b\u8005\u9700\u8981\u63d0\u4ea4\u6a21\u578b\u6587\u4ef6\u548c\u53c2\u6570\u6587\u4ef6\u81f3\u6587\u4ef6\u5939\u201calgorithm\/models\/\u201d\u4e0b\uff0c\u6a21\u578b\u6587\u4ef6\u547d\u540d\u65b9\u5f0f\u4e3anetwork.py\uff0c\u53c2\u6570\u6587\u4ef6\u547d\u540dpytorch\u5bf9\u5e94model.pth\uff0ctensorflow\u5bf9\u5e94model.h5\u3002\u6a21\u578b\u53c2\u6570\u6587\u4ef6\u5927\u5c0f\u9650\u5236\u4e3a50M\uff1b<br>3. \u82e5\u4f7f\u7528\u975eAI\u65b9\u6cd5\uff0c\u7b97\u6cd5\u6587\u4ef6\u63d0\u4ea4\u81f3\u4ee5\u4e0a\u76f8\u540c\u8def\u5f84\uff0c\u6587\u4ef6\u547d\u540d\u4e3aalg.py\uff1b<br>4. \u53c2\u8d5b\u8005\u9700\u8981\u63d0\u4ea4\u6587\u6863\u62a5\u544a\u9610\u8ff0\u6240\u4f7f\u7528\u65b9\u6cd5\uff0c\u6587\u6863\u5b58\u653e\u5728algorithm\u4e8c\u7ea7\u76ee\u5f55\u4e0b\uff1b<br>5. data\u548calgorithm\u6309\u7167\u4e8c\u7ea7\u76ee\u5f55\u7ed3\u6784\u8fdb\u884c\u653e\u7f6e\uff0c\u5c06\u4e8c\u7ea7\u76ee\u5f55\u653e\u7f6e\u4e8e\u547d\u540d\u4e3aresult\u7684\u4e00\u7ea7\u76ee\u5f55\u5185\uff0c\u5c06\u4e00\u7ea7\u76ee\u5f55result\u538b\u7f29\u6210.zip\u683c\u5f0f\u4e0a\u4f20\uff1b<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"475\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-1024x475.png\" alt=\"\" class=\"wp-image-4680\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-1024x475.png 1024w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-300x139.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-768x356.png 768w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image.png 1119w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>\u8d5b\u9898\u7b80\u4ecb\u672c\u6b21\u9898\u76ee\u56f4\u7ed5\u624b\u673a\u56fe\u7247RAW\u57df\u53bb\u566a\u95ee\u9898\uff0c\u53c2\u8d5b\u8005\u7b97\u6cd5\u65b9\u6848\u4f7f\u7528\u57fa\u4e8eAI\u6216\u4f20\u7edf\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u5747\u53ef\u3002<br>\u6bd4\u8d5b\u76ee\u6807\u662f\u63d0\u5347\u4e3e\u529e\u65b9\u63d0\u4f9b\u7ed9\u53c2\u8d5b\u800510\u5f20noisy\u56fe\u7247\u7684PSNR\u548cSSIM\u6307\u6807\u3002\u4e3a\u4e86\u65b9\u4fbf\u53c2\u8d5b\u8005\u8f7b\u677e\u4e0a\u624b\u6d41\u7a0b\uff0c\u4e3e\u529e\u65b9\u4e3a\u53c2\u8d5b\u8005\u63d0\u4f9bbaseline\u4ee3\u7801\u793a\u4f8b\uff0c\u4ee5\u53catraining dataset(200\u5f20\u56fe\u7247)\u4ee5\u5e2e\u52a9\u53c2\u8d5b\u8005\u66f4\u597d\u5730\u63d0\u5347\u7b97\u6cd5\u6027\u80fd\u3002\u6839\u636e\u53c2\u8d5b\u8005\u6240\u63d0\u4ea4\u7b97\u6cd5\u7684\u539f\u521b\u6027\uff0c\u989d\u5916\u67095% bonus\u5206\u6570\u6d6e\u52a8\u3002<\/p>\n\n\n\n<p>\u6bd4\u8d5b\u6392\u540d 55\/1159<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"53\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-2-1024x53.png\" alt=\"\" class=\"wp-image-4682\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-2-1024x53.png 1024w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-2-300x16.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-2-768x40.png 768w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-2.png 1137w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"has-light-pink-background-color has-background\">\u9879\u76ee\u53c2\u8003 \u8bba\u6587\uff1a Simple Baselines for Image Restoration  <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\u53c2\u8003\uff1aSimple Baselines for Image Restoration  \n\u5355\u4f4d\uff1a\u65f7\u89c6  \n\u4ee3\u7801\uff1ahttps:\/\/github.com\/megvii-research\/NAFNet  \n\u8bba\u6587\uff1ahttps:\/\/arxiv.org\/abs\/2204.0467\n<\/code><\/pre>\n\n\n\n<h2>\u9879\u76ee\u4ecb\u7ecd\uff1a<\/h2>\n\n\n\n<h2>\u4e00\u3001\u7f51\u7edc\u7ed3\u6784:<\/h2>\n\n\n\n<p>1.1\u4f7f\u7528\u7c7bUnet\u7ed3\u6784\uff1a<\/p>\n\n\n\n<p>\u5982\u4e0b\u56fe\uff0cUnet \u7f51\u7edc\u7ed3\u6784\u662f\u5bf9\u79f0\u7684\uff0c\u5f62\u4f3c\u82f1\u6587\u5b57\u6bcd U \u6240\u4ee5\u88ab\u79f0\u4e3a Unet\u3002\u901a\u8fc7\u62fc\u63a5\u7684\u65b9\u5f0f\u5c06\u4e0d\u540c\u5c42\u6b21\u7684\u7279\u5f81\u8fdb\u884c\u901a\u9053\u62fc\u63a5\u3002\u5176\u4e2d\u7f51\u7edc\u4e2d\u4e3b\u8981\u4f7f\u7528\u4e86NAFBlock\u5757\u3002U-Net\u548cFCN\u975e\u5e38\u7684\u76f8\u4f3c\uff0cU-Net\u6bd4FCN\u7a0d\u665a\u63d0\u51fa\u6765\uff0c\u4f46\u90fd\u53d1\u8868\u57282015\u5e74\uff0c\u548cFCN\u76f8\u6bd4\uff0cU-Net\u7684\u7b2c\u4e00\u4e2a\u7279\u70b9\u662f\u5b8c\u5168\u5bf9\u79f0\uff0c\u4e5f\u5c31\u662f\u5de6\u8fb9\u548c\u53f3\u8fb9\u662f\u5f88\u7c7b\u4f3c\u7684\uff0c\u800cFCN\u7684decoder\u76f8\u5bf9\u7b80\u5355\uff0c\u53ea\u7528\u4e86\u4e00\u4e2adeconvolution\uff08\u53cd\u5377\u79ef\uff09\u7684\u64cd\u4f5c\uff0c\u4e4b\u540e\u5e76\u6ca1\u6709\u8ddf\u4e0a\u5377\u79ef\u7ed3\u6784\u3002\u7b2c\u4e8c\u4e2a\u533a\u522b\u5c31\u662fskip connection\uff0cFCN\u7528\u7684\u662f\u52a0\u64cd\u4f5c\uff08summation\uff09\uff0cU-Net\u7528\u7684\u662f\u53e0\u64cd\u4f5c\uff08concatenation\uff09\u3002\u8fd9\u4e9b\u90fd\u662f\u7ec6\u8282\uff0c\u91cd\u70b9\u662f\u5b83\u4eec\u7684\u7ed3\u6784\u7528\u4e86\u4e00\u4e2a\u6bd4\u8f83\u7ecf\u5178\u7684\u601d\u8def\uff0c\u4e5f\u5c31\u662f\u7f16\u7801\u548c\u89e3\u7801\uff08encoder-decoder\uff09\uff0c\u65e9\u57282006\u5e74\u5c31\u88abHinton\u5927\u795e\u63d0\u51fa\u6765\u53d1\u8868\u5728\u4e86nature\u4e0a.<\/p>\n\n\n\n<p>\u5f53\u65f6\u8fd9\u4e2a\u7ed3\u6784\u63d0\u51fa\u7684\u4e3b\u8981\u4f5c\u7528\u5e76\u4e0d\u662f\u5206\u5272\uff0c\u800c\u662f\u538b\u7f29\u56fe\u50cf\u548c\u53bb\u566a\u58f0\u3002\u8f93\u5165\u662f\u4e00\u5e45\u56fe\uff0c\u7ecf\u8fc7\u4e0b\u91c7\u6837\u7684\u7f16\u7801\uff0c\u5f97\u5230\u4e00\u4e32\u6bd4\u539f\u5148\u56fe\u50cf\u66f4\u5c0f\u7684\u7279\u5f81\uff0c\u76f8\u5f53\u4e8e\u538b\u7f29\uff0c\u7136\u540e\u518d\u7ecf\u8fc7\u4e00\u4e2a\u89e3\u7801\uff0c\u7406\u60f3\u72b6\u51b5\u5c31\u662f\u80fd\u8fd8\u539f\u5230\u539f\u6765\u7684\u56fe\u50cf\u3002\u8fd9\u6837\u7684\u8bdd\u6211\u4eec\u5b58\u4e00\u5e45\u56fe\u7684\u65f6\u5019\u5c31\u53ea\u9700\u8981\u5b58\u4e00\u4e2a\u7279\u5f81\u548c\u4e00\u4e2a\u89e3\u7801\u5668\u5373\u53ef\u3002\u8fd9\u4e2a\u60f3\u6cd5\u6211\u4e2a\u4eba\u8ba4\u4e3a\u662f\u5f88\u6f02\u4eae\u4e86\u3002\u540c\u7406\uff0c\u8fd9\u4e2a\u601d\u8def\u4e5f\u53ef\u4ee5\u7528\u5728\u539f\u56fe\u50cf\u53bb\u566a\uff0c\u505a\u6cd5\u5c31\u662f\u5728\u8bad\u7ec3\u7684\u9636\u6bb5\u5728\u539f\u56fe\u4eba\u4e3a\u7684\u52a0\u4e0a\u566a\u58f0\uff0c\u7136\u540e\u653e\u5230\u8fd9\u4e2a\u7f16\u7801\u89e3\u7801\u5668\u4e2d\uff0c\u76ee\u6807\u662f\u53ef\u4ee5\u8fd8\u539f\u5f97\u5230\u539f\u56fe\u3002<\/p>\n\n\n\n<p>\u540e\u6765\u628a\u8fd9\u4e2a\u601d\u8def\u88ab\u7528\u5728\u4e86\u56fe\u50cf\u5206\u5272\u7684\u95ee\u9898\u4e0a\uff0c\u4e5f\u5c31\u662f\u73b0\u5728\u6211\u4eec\u770b\u5230\u7684U-Net\u7ed3\u6784\uff0c\u5728\u5b83\u88ab\u63d0\u51fa\u7684\u4e09\u5e74\u4e2d\uff0c\u6709\u5f88\u591a\u5f88\u591a\u7684\u8bba\u6587\u53bb\u8bb2\u5982\u4f55\u6539\u8fdbU-Net\u6216\u8005FCN\uff0c\u4e0d\u8fc7\u8fd9\u4e2a\u5206\u5272\u7f51\u7edc\u7684\u672c\u8d28\u7684\u62d3\u6251\u7ed3\u6784\u662f\u6ca1\u6709\u6539\u52a8\u7684\u3002\u4e3e\u4f8b\u6765\u8bf4\uff0cICCV\u4e0a\u51ef\u660e\u5927\u795e\u63d0\u51fa\u7684Mask RCNN. \u76f8\u5f53\u4e8e\u4e00\u4e2a\u68c0\u6d4b\uff0c\u5206\u7c7b\uff0c\u5206\u5272\u7684\u96c6\u5927\u6210\u8005\uff0c\u6211\u4eec\u4ed4\u7ec6\u53bb\u770b\u5b83\u7684\u5206\u5272\u90e8\u5206\uff0c\u5176\u5b9e\u4f7f\u7528\u7684\u4e5f\u5c31\u662f\u8fd9\u4e2a\u7b80\u5355\u7684FCN\u7ed3\u6784\u3002\u8bf4\u660e\u4e86\u8fd9\u79cd\u201cU\u5f62\u201d\u7684\u7f16\u7801\u89e3\u7801\u7ed3\u6784\u786e\u5b9e\u975e\u5e38\u7684\u7b80\u6d01\uff0c\u5e76\u4e14\u6700\u5173\u952e\u7684\u4e00\u70b9\u662f\u597d\u7528\u3002<\/p>\n\n\n\n<p><strong>\u91c7\u7528Unet\u7684\u597d\u5904\u6211\u611f\u89c9\u662f\uff1a\u7f51\u7edc\u5c42\u8d8a\u6df1\u5f97\u5230\u7684\u7279\u5f81\u56fe\uff0c\u6709\u7740\u66f4\u5927\u7684\u89c6\u91ce\u57df\uff0c\u6d45\u5c42\u5377\u79ef\u5173\u6ce8\u7eb9\u7406\u7279\u5f81\uff0c\u6df1\u5c42\u7f51\u7edc\u5173\u6ce8\u672c\u8d28\u7684\u90a3\u79cd\u7279\u5f81\uff0c\u6240\u4ee5\u6df1\u5c42\u6d45\u5c42\u7279\u5f81\u90fd\u662f\u6709\u683c\u5b50\u7684\u610f\u4e49\u7684\uff1b\u53e6\u5916\u4e00\u70b9\u662f\u901a\u8fc7\u53cd\u5377\u79ef\u5f97\u5230\u7684\u66f4\u5927\u7684\u5c3a\u5bf8\u7684\u7279\u5f81\u56fe\u7684\u8fb9\u7f18\uff0c\u662f\u7f3a\u5c11\u4fe1\u606f\u7684\uff0c\u6bd5\u7adf\u6bcf\u4e00\u6b21\u4e0b\u91c7\u6837\u63d0\u70bc\u7279\u5f81\u7684\u540c\u65f6\uff0c\u4e5f\u5fc5\u7136\u4f1a\u635f\u5931\u4e00\u4e9b\u8fb9\u7f18\u7279\u5f81\uff0c\u800c\u5931\u53bb\u7684\u7279\u5f81\u5e76\u4e0d\u80fd\u4ece\u4e0a\u91c7\u6837\u4e2d\u627e\u56de\uff0c\u56e0\u6b64\u901a\u8fc7\u7279\u5f81\u7684\u62fc\u63a5\uff0c\u6765\u5b9e\u73b0\u8fb9\u7f18\u7279\u5f81\u7684\u4e00\u4e2a\u627e\u56de\u3002<\/strong><\/p>\n\n\n\n<p>\u6700\u540e\uff0c\u5c06\u7f51\u7edc\u8f93\u51fa\u548cinput\u505a\u52a0\u548c\uff0c\u8fd9\u6837\u5b9e\u9645\u4e0a\u662f\u7528\u7f51\u7edc\u505a\u566a\u58f0\u7684\u9884\u6d4b\uff0c\u60f3\u6bd4\u76f4\u63a5\u8f93\u51fa\u56fe\u50cf\uff0c\u8f93\u51fa\u566a\u58f0\u7684\u5b9e\u9645\u6548\u679c\u597d\uff0c\u4e2a\u4eba\u8ba4\u4e3a\uff0c\u5982\u679c\u8f93\u51fa\u7684\u662f\u56fe\u50cf\uff0c\u90a3\u4e48\u5373\u4f7f\u7528unet\u7ed3\u6784\uff0c\u5728\u8fdb\u884cconv\u3001layernormal\u8fc7\u7a0b\u4e2d\u8fd8\u4f1a\u9020\u6210\u56fe\u50cf\u7684\u7ec6\u8282\u7279\u5f81\u635f\u5931\uff0c\u5bf9\u4e8e\u751f\u6210\u7684\u56fe\u50cf\u7ec6\u8282\u65b9\u9762\u4f1a\u5dee\u4e00\u4e9b\u3002\u603b\u4e4b\uff0c\u6211\u8ba4\u4e3a\u76f4\u63a5\u9884\u6d4b\u56fe\u50cf\u7684task\u4f1a\u6bd4\u9884\u6d4b\u566a\u58f0\u7684\u96be\u5ea6\u5927\u3002<\/p>\n\n\n\n<p>\u4e0b\u56fe1\u662f\u672c\u6b21\u8bbe\u8ba1\u7684\u56fe\u50cf\u53bb\u566a\u7f51\u7edc\u7ed3\u6784\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"909\" height=\"477\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-33.png\" alt=\"\" class=\"wp-image-4896\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-33.png 909w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-33-300x157.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-33-768x403.png 768w\" sizes=\"(max-width: 909px) 100vw, 909px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center\">Figure 1 NFnet\u7f51\u7edc\u7ed3\u6784<\/p>\n\n\n\n<h2>\u4e8c\u3001NAFBlock\u5757<\/h2>\n\n\n\n<p>\uff08\u4f7f\u7528\u8bba\u6587Simple Baselines for Image Restoration\u4e2d\u7684NAFBlock\u6a21\u5757\uff09<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"792\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-34-1024x792.png\" alt=\"\" class=\"wp-image-4898\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-34-1024x792.png 1024w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-34-300x232.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-34-768x594.png 768w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-34.png 1027w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>NAFBlock\u7ed3\u6784\u4ecb\u7ecd\uff1a<\/p>\n\n\n\n<ol type=\"1\"><li>Normalization\uff1aLayer Normalization<\/li><li>\u52a0\u901f\u8bad\u7ec3(\u53ef\u4ee5\u4f7f\u7528\u66f4\u5927\u7684learning rate)<\/li><li>\u9632\u6b62exploding\/vanishing gradients.<\/li><li>\u51cf\u5c0f\u53c2\u6570\u7684initialization\u5bf9\u8bad\u7ec3\u7684\u5f71\u54cd<\/li><li>\u63d0\u9ad8\u8bad\u7ec3\u6548\u679c<\/li><li>layerNorm\u5173\u6ce8\u6574\u5e45\u56fe\uff0c\u4e5f\u6ca1\u6709\u8d85\u8fc7\u5355\u5f20\u7684\u8303\u56f4\uff0cLN\u5c06\u6bcf\u4e2a\u8bad\u7ec3\u6837\u672c\u90fd\u5f52\u4e00\u5316\u5230\u4e86\u76f8\u540c\u7684\u5206\u5e03\u4e0a\uff0c\u67d0\u79cd\u610f\u4e49\u4e0a\u907f\u514d\u4e86\u5e73\u6ed1<\/li><\/ol>\n\n\n\n<p>\u5bf9\u4e8eNormalization\uff0c\u6587\u7ae0\u91c7\u7528\u4e86Transformer\u91cc\u88ab\u901a\u5e38\u91c7\u7528\u7684LayerNorm\uff0c\u5e76\u901a\u8fc7\u5b9e\u9a8c\u53d1\u73b0\u5176\u80fd\u63d0\u70b9\u3002\u5176\u5b9e\u4f20\u7edf\u610f\u4e49\u4e0a\uff0c\u9664\u4e86\u65e9\u671f\u7684\u65b9\u6cd5\uff0c\u5e95\u5c42\u89c6\u89c9\u4e00\u822c\u662f\u4e0d\u592a\u4f1a\u589e\u52a0\u5f52\u4e00\u5316\u5c42\uff0c\u8ba4\u4e3a\u5176\u4f1a\u964d\u70b9\u800c\u4e14\u8ba9\u56fe\u50cf\u6a21\u7cca\uff0c\u6211\u4e2a\u4eba\u7406\u89e3\u8fd9\u53ef\u80fd\u548cBatchNorm\u7684\u7279\u6027\u6709\u5173\uff0c\u4e00\u65b9\u9762BatchNorm\u672c\u8eab\u8bad\u7ec3\u6d4b\u8bd5\u9636\u6bb5\u7531\u4e8e\u7edf\u8ba1\u91cf\u4e0d\u540c\uff0c\u5c31\u4f1a\u5bfc\u81f4\u9886\u57df\u4e0d\u9002\u5e94\u95ee\u9898\u3002\u53e6\u5916\u4e0d\u540c\u4e8ehigh-level task\u503e\u5411\u4e8e\u5bfb\u627e\u4e00\u81f4\u6027\u8868\u793a\uff0c\u5e95\u5c42\u89c6\u89c9\u7684\u4efb\u52a1\u4e0e\u4e4b\u76f8\u53cd\uff0c\u5f80\u5f80\u662f\u503e\u5411\u4e8e\u5b66\u4e60\u56fe\u7247\u7279\u5b9a\u6027\u4ee5\u589e\u5f3a\u7ec6\u8282\u7684\u6062\u590d\u6548\u679c\uff08\u6bd4\u5982\u4e4b\u524d\u6709\u4eba\u901a\u8fc7\u6355\u83b7\u56fe\u50cf\u5206\u5e03\uff08\u6b63\u6001\u5206\u5e03\uff09\u7684sigma\u4ee5\u589e\u5f3a\u8fb9\u7f18\u533a\u57df\u7684\u6548\u679c\uff09\uff0cbatchNorm\u7531\u4e8e\u662fbatch\u5185\u505aattention\uff0c\u5176\u5b9e\u5f88\u5bb9\u6613\u5c06\u5176\u4ed6\u56fe\u7247\u7684\u4fe1\u606f\u5f15\u5165\uff0c\u5ffd\u7565\u4e86\u6062\u590d\u56fe\u50cf\u7684\u7279\u5b9a\u4fe1\u606f\uff0c\u5bfc\u81f4\u6027\u80fd\u4e0b\u964d\u3002\u6240\u4ee5\u4e4b\u524d\u5e95\u5c42\u89c6\u89c9\u91cc\u9762\u7528\u7684\u6bd4\u8f83\u591a\u7684norm\u662finstance Norm\uff08\u6bd4\u8f83\u591a\u7684\u662f\u5728\u98ce\u683c\u8fc1\u79fb\uff0c\u56fe\u50cf\u6062\u590d\u8fd9\u8fb9\u6709HI-Net\u5c31\u662f\u7528IN\uff09\uff0c\u56e0\u4e3a\u53ea\u5173\u6ce8\u540c\u4e00\u4e2a\u56fe\u7247\u540c\u4e00channel\u5185\u7684\u4fe1\u606f\uff0c\u6240\u4ee5\u67d0\u79cd\u610f\u4e49\u4e0a\u907f\u514d\u4e86\u5e73\u6ed1\uff0clayerNorm\u5173\u6ce8\u6574\u5e45\u56fe\uff0c\u4e5f\u6ca1\u6709\u8d85\u8fc7\u5355\u5f20\u7684\u8303\u56f4\uff0c\u6240\u4ee5\u80fd\u591fwork\u8fd8\u662f\u86eemake sense\u7684\u3002<\/p>\n\n\n\n<p>\u5f52\u4e00\u5316\u6280\u672f\u5728high-level\u4efb\u52a1\u4e2d\u5df2\u88ab\u5e7f\u6cdb\u5e94\u7528\uff0c\u4f46\u5728low-level\u4efb\u52a1\u4e2d\u5e94\u7528\u6781\u5c11\u3002\u4f46\u662f\uff0c\u4f9d\u6258\u4e8eTransformer\uff0cLN\u5f97\u5230\u4e86\u8d8a\u6765\u8d8a\u591a\u7684\u5e94\u7528\u3002\u57fa\u4e8e\u8be5\u4e8b\u5b9e\uff0c\u6211\u4eec\u731c\u60f3\uff1aLN\u53ef\u80fd\u662f\u8fbe\u6210SOTA\u590d\u539f\u5668\u7684\u5173\u952e\uff0c\u6545\u5728\u4e0a\u8ff0\u6a21\u5757\u4e2d\u6dfb\u52a0\u4e86LN(\u89c1\u4e0a\u9762\u56fe\u793a)\u3002LN\u7684\u5f15\u5165\u4f7f\u5f97\u8bad\u7ec3\u66f4\u5e73\u6ed1\uff0c\u751a\u81f3\u53ef\u4ee5\u5c06\u5b66\u4e60\u7387\u653e\u592710\u500d\u3002\u66f4\u5927\u7684\u5b66\u4e60\u7387\u53ef\u4ee5\u5e26\u6765\u663e\u8457\u6027\u80fd\u63d0\u5347\u3002<\/p>\n\n\n\n<p>\u5728Transformer\u4e2d\uff0c\u6570\u636e\u8fc7Attention\u5c42\u548cFFN\u5c42\u540e\uff0c\u90fd\u4f1a\u7ecf\u8fc7\u4e00\u4e2aAdd &amp; Norm\u5904\u7406\u3002\u5176\u4e2d\uff0cAdd\u4e3aresidule block\uff08\u6b8b\u5dee\u6a21\u5757\uff09\uff0c\u6570\u636e\u5728\u8fd9\u91cc\u8fdb\u884cresidule connection\uff08\u6b8b\u5dee\u8fde\u63a5\uff09\u3002\u800cNorm\u5373\u4e3aNormalization\uff08\u6807\u51c6\u5316\uff09\u6a21\u5757\u3002Transformer\u4e2d\u91c7\u7528\u7684\u662fLayer Normalization\uff08\u5c42\u6807\u51c6\u5316\uff09\u65b9\u5f0f\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"909\" height=\"280\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-35.png\" alt=\"\" class=\"wp-image-4900\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-35.png 909w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-35-300x92.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-35-768x237.png 768w\" sizes=\"(max-width: 909px) 100vw, 909px\" \/><\/figure>\n\n\n\n<p>\u5728\u56fe\u7247\u89c6\u9891\u5206\u7c7b\u7b49\u7279\u5f81\u63d0\u53d6\u7f51\u7edc\u4e2d\u5927\u591a\u6570\u60c5\u51b5BN\u6548\u679c\u4f18\u4e8eIN\uff0c\u5728\u751f\u6210\u5f0f\u7c7b\u4efb\u52a1\u4e2d\u7684\u7f51\u7edcIN\u4f18\u4e8eBN\u3002<\/p>\n\n\n\n<p>BN\u9002\u7528\u4e8e\u5224\u522b\u6a21\u578b\u4e2d\uff0c\u6bd4\u5982\u56fe\u7247\u5206\u7c7b\u6a21\u578b\u3002\u56e0\u4e3aBN\u6ce8\u91cd\u5bf9\u6bcf\u4e2abatch\u8fdb\u884c\u5f52\u4e00\u5316\uff0c\u4ece\u800c\u4fdd\u8bc1\u6570\u636e\u5206\u5e03\u7684\u4e00\u81f4\u6027\uff0c\u800c\u5224\u522b\u6a21\u578b\u7684\u7ed3\u679c\u6b63\u662f\u53d6\u51b3\u4e8e\u6570\u636e\u6574\u4f53\u5206\u5e03\u3002\u4f46\u662fBN\u5bf9batchsize\u7684\u5927\u5c0f\u6bd4\u8f83\u654f\u611f\uff0c\u7531\u4e8e\u6bcf\u6b21\u8ba1\u7b97\u5747\u503c\u548c\u65b9\u5dee\u662f\u5728\u4e00\u4e2abatch\u4e0a\uff0c\u6240\u4ee5\u5982\u679cbatchsize\u592a\u5c0f\uff0c\u5219\u8ba1\u7b97\u7684\u5747\u503c\u3001\u65b9\u5dee\u4e0d\u8db3\u4ee5\u4ee3\u8868\u6574\u4e2a\u6570\u636e\u5206\u5e03\uff1b<\/p>\n\n\n\n<p>IN\u9002\u7528\u4e8e\u751f\u6210\u6a21\u578b\u4e2d\uff0c\u6bd4\u5982\u56fe\u7247\u98ce\u683c\u8fc1\u79fb\u3002\u56e0\u4e3a\u56fe\u7247\u751f\u6210\u7684\u7ed3\u679c\u4e3b\u8981\u4f9d\u8d56\u4e8e\u67d0\u4e2a\u56fe\u50cf\u5b9e\u4f8b\uff0c\u6240\u4ee5\u5bf9\u6574\u4e2abatch\u5f52\u4e00\u5316\u4e0d\u9002\u5408\u56fe\u50cf\u98ce\u683c\u5316\u4e2d\uff0c\u5728\u98ce\u683c\u8fc1\u79fb\u4e2d\u4f7f\u7528Instance Normalization\u4e0d\u4ec5\u53ef\u4ee5\u52a0\u901f\u6a21\u578b\u6536\u655b\uff0c\u5e76\u4e14\u53ef\u4ee5\u4fdd\u6301\u6bcf\u4e2a\u56fe\u50cf\u5b9e\u4f8b\u4e4b\u95f4\u7684\u72ec\u7acb\u3002<\/p>\n\n\n\n<ul><li>Activation\uff1asimple gate \u5f15\u5165\u975e\u7ebf\u6027<\/li><\/ul>\n\n\n\n<p>\uff08\u5bf9\u7279\u5f81\u8fdb\u884c\u4e86channel-split\uff0c\u5206\u6210\u4e24\u4e2aC\/2\u4e2a\u901a\u9053\u7684\u7279\u5f81\uff0c\u5e76\u76f8\u4e58\uff09<\/p>\n\n\n\n<p>\u5c3d\u7ba1ReLU\u662f\u6700\u5e38\u7528\u7684\u6fc0\u6d3b\u51fd\u6570\uff0c\u73b0\u6709SOTA\u65b9\u6848\u4e2d\u91c7\u7528GELU\u8fdb\u884c\u4ee3\u66ff\u3002\u7531\u4e8eGELU\u53ef\u4ee5\u4fdd\u6301\u964d\u566a\u6027\u80fd\u76f8\u5f53\u4e14\u5927\u5e45\u63d0\u5347\u53bb\u6a21\u7cca\u6027\u80fd\uff0c\u6545\u6211\u4eec\u91c7\u7528GELU\u66ff\u4ee3ReLU,\u4f46\u4f5c\u8005\u8ba4\u4e3a GELU\u592a\u590d\u6742\uff1a\u56e0\u6b64\u63d0\u51fa\u4e86\u7b80\u5316\u7248\u7684GELU\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"619\" height=\"70\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-36.png\" alt=\"\" class=\"wp-image-4901\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-36.png 619w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-36-300x34.png 300w\" sizes=\"(max-width: 619px) 100vw, 619px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"909\" height=\"167\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-37.png\" alt=\"\" class=\"wp-image-4902\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-37.png 909w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-37-300x55.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-37-768x141.png 768w\" sizes=\"(max-width: 909px) 100vw, 909px\" \/><\/figure>\n\n\n\n<p>\u4f5c\u8005\u4e5f\u662f\u4eceHigh-Level Task \u627e\u5230\u7684\u7075\u611f\uff0c\u5c06\u73b0\u5728\u5927\u706b\u7684GLU\u548cGELU\u5f15\u5165\u5e76\u505a\u4e86\u7b80\u5316\u3002 \u6587\u7ae0\u5148\u7ed9\u51fa\u4e86GLU\u7684\u6570\u5b66\u5f62\u5f0f\uff1a\uff0c\u4e4b\u540e\u6587\u7ae0\u8ba4\u4e3aGELU\u662fGLU\u7684\u4e00\u79cd\u7279\u6b8a\u60c5\u51b5\uff08\u8fd9\u4e2a\u53ef\u4ee5\u770b\u539f\u6587\uff0c\u6bd4\u8f83\u76f4\u89c2\uff09\uff0c\u4e8e\u662f\u53ea\u5173\u6ce8\u4e8eGLU\u672c\u8eab\u3002\u867d\u7136GLU\u53ef\u4ee5\u63d0\u5347\u6a21\u578b\u6548\u679c\uff0c\u4f46\u662f\u4e5f\u4f1a\u589e\u52a0\u8ba1\u7b97\u91cf\uff0c\u4e8e\u662f\u4f5c\u8005\u4e3a\u964d\u4f4e\u8ba1\u7b97\u91cf\uff0c\u6240\u4ee5\u5bf9GLU\u8fdb\u884c\u4e86\u7b80\u5316\u3002GLU\u7684\u8ba1\u7b97\u91cf\u4e3b\u8981\u6765\u81ea\u4e8esigmoid\u548c\u6620\u5c04\u51fd\u6570\uff08\u4e0a\u56fe\uff09\u3002\u56e0\u4e3aGLU\u672c\u8eab\u662f\u5177\u5907\u975e\u7ebf\u6027\u8fd9\u4e00\u6027\u8d28\u7684\uff08\u6211\u4e2a\u4eba\u7406\u89e3\u662f\uff08\u5143\u7d20\u79ef\uff09element-wise multiplication\u5f15\u5165\u7684\uff09\uff0c\u6240\u4ee5\u6587\u7ae0\u5220\u9664\u4e86sigmoid\u3002\u4e3a\u4e86\u51cf\u5c11\u8ba1\u7b97\u91cf\uff0c\u6620\u5c04\u51fd\u6570\u66f4\u662f\u76f4\u63a5\u5220\u9664\uff0c\u540c\u65f6\u5bf9\u7279\u5f81\u8fdb\u884c\u4e86channel-split\uff0c\u5206\u6210\u4e24\u4e2aC\/2\u4e2a\u901a\u9053\u7684\u7279\u5f81\uff0c\u5e76\u76f8\u4e58\uff0c\u5177\u4f53\u662f\u4e0a\u9762\u7684\u56fe\u3002 \u7531\u4e8e\u8fd9\u4e2a\u7b80\u5316\u7684simple gate\u5f15\u5165\u4e86\u975e\u7ebf\u6027\uff0c\u6240\u4ee5\u5e38\u7528\u7684ReLU\u81ea\u7136\u4e5f\u4e0d\u9700\u8981\u518d\u52a0\u5165\u5230\u7f51\u7edc\u4e2d\u4e86\uff0c\u8fd9\u4e5f\u5c31\u662f\u4e3a\u4ec0\u4e48\u8fd9\u7bc7\u6587\u7ae0\u63d0\u51fa\u7684\u65b9\u6cd5\u53eb\u505a <strong>Nonlinear Activation Free Network \uff08NAFNet\uff09<\/strong>\u3002<\/p>\n\n\n\n<p>\u8865\u5145\uff1achannel-split \u601d\u60f3\u6765\u81eaChannel-Wise Convolutions<\/p>\n\n\n\n<p>\u3010ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions\uff1a\u8bba\u6587\u63d0\u51fachannel-wise\u5377\u79ef\u7684\u6982\u5ff5\uff0c\u5c06\u8f93\u5165\u8f93\u51fa\u7684\u7ef4\u5ea6\u8fde\u63a5\u8fdb\u884c\u7a00\u758f\u5316\u800c\u975e\u5168\u8fde\u63a5\uff0c\u533a\u522b\u4e8e\u5206\u7ec4\u5377\u79ef\u7684\u4e25\u683c\u5206\u7ec4\uff0c\u8ba9\u5377\u79ef\u5728channel\u7ef4\u5ea6\u4e0a\u8fdb\u884c\u6ed1\u52a8\uff0c\u80fd\u591f\u66f4\u597d\u5730\u4fdd\u7559channel\u95f4\u7684\u4fe1\u606f\u4ea4\u6d41\u3002\u57fa\u4e8echannel-wise\u5377\u79ef\u7684\u601d\u60f3\uff0c\u8bba\u6587\u8fdb\u4e00\u6b65\u63d0\u51fa\u4e86channel-wise\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\uff0c\u5e76\u57fa\u4e8e\u8be5\u7ed3\u6784\u66ff\u6362\u7f51\u7edc\u6700\u540e\u7684\u5168\u8fde\u63a5\u5c42+\u5168\u5c40\u6c60\u5316\u7684\u64cd\u4f5c\uff0c\u642d\u5efa\u4e86ChannelNets\u3002Channel-wise\u5377\u79ef\u7684\u6838\u5fc3\u5728\u4e8e\u8f93\u5165\u548c\u8f93\u51fa\u8fde\u63a5\u7684\u7a00\u758f\u5316\uff0c\u6bcf\u4e2a\u8f93\u51fa\u4ec5\u4e0e\u90e8\u5206\u8f93\u5165\u76f8\u8fde\uff0c\u6982\u5ff5\u4e0a\u533a\u522b\u4e8e\u5206\u7ec4\u5377\u79ef\uff0c\u6ca1\u6709\u5bf9\u8f93\u5165\u8fdb\u884c\u4e25\u683c\u7684\u533a\u5206\uff0c\u800c\u662f\u4ee5\u4e00\u5b9a\u7684stride\u53bb\u91c7\u6837\u591a\u4e2a\u76f8\u5173\u8f93\u5165\u8fdb\u884c\u8f93\u51fa(\u5728channel\u7ef4\u5ea6\u6ed1\u52a8)\uff0c\u80fd\u591f\u964d\u5c11\u53c2\u6570\u91cf\u4ee5\u53ca\u4fdd\u8bc1channel\u95f4\u4e00\u5b9a\u7a0b\u5ea6\u7684\u4fe1\u606f\u6d41\u3002\u3011<\/p>\n\n\n\n<p>GELU\u4e0eGLU\u7684\u5b9e\u73b0\u53ef\u4ee5\u53d1\u73b0\uff1aGELU\u662fGLU\u7684\u4e00\u79cd\u7279\u4f8b\u3002\u6211\u4eec\u4ece\u53e6\u4e00\u4e2a\u89d2\u5ea6\u731c\u60f3\uff1aGLU\u53ef\u89c6\u4f5c\u4e00\u79cd\u5e7f\u4e49\u6fc0\u6d3b\u51fd\u6570\uff0c\u5b83\u662f\u53ef\u4ee5\u7528\u4e8e\u66ff\u4ee3\u975e\u7ebf\u6027\u6fc0\u6d3b\u51fd\u6570\u3002\u63d0\u51fa\u4e86\u4e00\u79cd\u7b80\u5316\u7248GLU\u53d8\u79cd(\u89c1\u4e0a\u56fe)\uff1a\u76f4\u63a5\u5c06\u7279\u5f81\u6cbf\u901a\u9053\u7ef4\u5ea6\u5206\u6210\u4e24\u90e8\u5206\u5e76\u76f8\u4e58\u3002<\/p>\n\n\n\n<ul><li>Simplified Channel Attention<\/li><\/ul>\n\n\n\n<p>\u6ce8\u610f\u529b\u673a\u5236\u53ef\u4ee5\u8bf4\u662f\u8fd1\u5e74\u6765\u6700\u706b\u70ed\u7684\u7814\u7a76\u9886\u57df\u4e4b\u4e00\uff0c\u5176\u6709\u6548\u6027\u5f97\u5230\u4e86\u5145\u5206\u7684\u9a8c\u8bc1<\/p>\n\n\n\n<p>\u901a\u8fc7\u4fdd\u7559\u901a\u9053\u6ce8\u610f\u529b\u7684\u4e24\u4e2a\u91cd\u8981\u4f5c\u7528(\u5168\u5c40\u4fe1\u606f\u805a\u5408\u3001\u901a\u9053\u4fe1\u606f\u4ea4\u4e92)\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u5982\u4e0a\u56fe\u7684\u7b80\u5316\u7248\u901a\u9053\u6ce8\u610f\u529b\u3002<\/p>\n\n\n\n<p>\u5bf9\u4e8eattention\uff0c\u4e0a\u8ff0\u7684simple Gate\u64cd\u4f5c\u867d\u7136\u53ef\u4ee5\u6709\u6548\u51cf\u5c11\u8ba1\u7b97\u91cf\uff0c\u4f46\u662f\u4f5c\u8005\u8ba4\u4e3achannel-wise\u7684\u64cd\u4f5c\uff08\u5bfc\u81f4channel\u95f4\u7684\u4fe1\u606f\u963b\u9694\uff09\u4e22\u5931\u4e86channel\u4e4b\u95f4\u7684\u4fe1\u606f\uff0c\u6240\u4ee5\u5728\u540e\u9762\u7684attention\u4e0a\uff0c\u4f5c\u8005\u4f7f\u7528\u4e86\u7b80\u5316\u7684channel attention\uff0c\u51cf\u5c11\u8ba1\u7b97\u91cf\u7684\u540c\u65f6\u5f15\u5165channel\u7684\u4ea4\u4e92\uff0c\u8fd9\u4e2a\u770b\u56fe\u5c31\u53ef\u4ee5\u76f4\u63a5\u660e\u767d\u3002\u8fd9\u4e2a\u5176\u5b9e\u5bf9\u6211\u4e2a\u4eba\u6709\u70b9\u542f\u53d1\uff0c\u56e0\u4e3a\u4eceswin\u5230restormer\uff08\u7528\u4e8e\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u6062\u590d\u7684\u9ad8\u6548Transformer\uff09\uff0c\u591a\u5c11\u80fd\u9690\u9690\u7684\u611f\u53d7\u5230\uff0c\u5176\u5b9etranformer\u7684\u5168\u5c40attention\u53ef\u80fd\u6ca1\u6709\u60f3\u8c61\u7684\u90a3\u4e48\u91cd\u8981\uff0cswin\u91cc\u5207\u6210window-based\u4ecd\u7136\u53ef\u4ee5\u4fdd\u6301\u5f88\u597d\u7684\u6548\u679c\uff0crestormer\u91cc\u9762\u5e72\u8106\u653e\u5f03\u4e86spatial\u7684MSA\uff08\u591a\u5934self\u6ce8\u610f\u529b\uff09\u800c\u4f7f\u7528\u6df1\u5ea6\u5377\u79ef\u548c\u4f20\u7edf\u7684spatial attention\uff08\u7a7a\u95f4\u6ce8\u610f\u529b\uff09\uff0c\u6709\u53ef\u80fdCA\u5bf9\u6062\u590d\u4efb\u52a1\u66f4\u91cd\u8981\u4e00\u4e9b\uff08\u6709\u5f85\u8bc1\u660e\uff09\u3002<\/p>\n\n\n\n<p>4\u30011*1\u5377\u79ef<\/p>\n\n\n\n<p>1&#215;1\u5377\u79ef\u5b9e\u9645\u4e0a\u662f\u5bf9\u6bcf\u4e2a\u50cf\u7d20\u70b9\uff0c\u5728\u4e0d\u540c\u7684channels\u4e0a\u8fdb\u884c\u7ebf\u6027\u7ec4\u5408\uff08\u4fe1\u606f\u6574\u5408\uff09\uff0c\u4e14\u4fdd\u7559\u4e86\u56fe\u7247\u7684\u539f\u6709\u5e73\u9762\u7ed3\u6784\uff0c\u8c03\u63a7depth\uff0c\u4ece\u800c\u5b8c\u6210\u5347\u7ef4\u6216\u964d\u7ef4\u7684\u529f\u80fd\u3002<\/p>\n\n\n\n<p>\u6700\u540e\uff0c\u6709\u4e86\u4e0a\u8ff0\u7684\u57fa\u672c\u6539\u8fdb\uff0c\u5e76\u5c06\u4e0a\u9762\u7684\u6a21\u5757\u7ec4\u5408\u5728\u4e86\u4e00\u8d77\u3002<\/p>\n\n\n\n<h2>\u5176\u4ed6\u8bf4\u660e\uff1a<\/h2>\n\n\n\n<p>1\u3001\u53c2\u8003\u8bba\u6587\uff1a<\/p>\n\n\n\n<p>Simple Baselines for Image Restoration\uff0c\u662f\u76ee\u524d\u53bb\u566a\u6548\u679c\u6bd4\u8f83\u597d\u7684\u7f51\u7edc\u3002<\/p>\n\n\n\n<p>Github\u4ee3\u7801\u5b9e\u73b0\uff1a<a href=\"https:\/\/github.com\/megvii-research\/NAFNet\">https:\/\/github.com\/megvii-research\/NAFNet<\/a><\/p>\n\n\n\n<p>2\u3001\u635f\u5931\u51fd\u6570\uff1a<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \u4f7f\u7528L1\u635f\u5931\u3001mse\u635f\u5931\uff08Fourier_loss\uff09\u3001<\/p>\n\n\n\n<p>psnrloss\u3010\u53c2\u8003<a href=\"https:\/\/github.com\/megvii-research\/NAFNet\">https:\/\/github.com\/megvii-research\/NAFNet<\/a>\u4e2d\u63d0\u4f9b\u7684psnrloss\u3011\u3001\u4ee5\u53ca\u76f8\u90bb\u50cf\u7d20\u635f\u5931<\/p>\n\n\n\n<p>L1\u635f\u5931\uff1a\u7528\u6765\u9884\u6d4bgenerate\u548creal \u4e4b\u95f4\u7684\u50cf\u7d20\u7ea7\u522b\u8bef\u5dee<\/p>\n\n\n\n<p>MSE\u635f\u5931\uff08Fourier_loss\uff09\uff1a\u8ba1\u7b97generate\u548creal\u7684fft\u53d8\u6362\u540e\u7684\u9891\u57df\u4fe1\u606f\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"909\" height=\"697\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-38.png\" alt=\"\" class=\"wp-image-4903\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-38.png 909w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-38-300x230.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-38-768x589.png 768w\" sizes=\"(max-width: 909px) 100vw, 909px\" \/><\/figure>\n\n\n\n<p>\u53c2\u8003\u8bba\u6587\uff1aFouier Space Losses for Efficient Perceptual Image Super-Resolution\uff0c\u5728\u6539\u8bba\u6587\u4e2d\u5229\u7528transformer\u5b9e\u73b0\u56fe\u50cf\u53bb\u96e8\uff0c\u63d0\u51fa\u4e86Fourier Space Losses\uff0c\u5355\u5f20\u56fe\u50cf\u8d85\u5206\u65b9\u6cd5\u5728\u91cd\u6784\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u65f6\u7f3a\u5931\u9ad8\u9891\u7ec6\u8282\u3002\u8fd9\u901a\u5e38\u901a\u8fc7\u6709\u76d1\u7763\u7684\u8bad\u7ec3\u6765\u6267\u884c\uff0c\u5176\u4e2d\u4f7f\u7528\u5df2\u77e5\u6838\u5bf9\u771f\u5b9e\u56fe\u50cf y \u8fdb\u884c\u4e0b\u91c7\u6837\uff0c\u4f8b\u5982 bicubic\uff0c\u5f97\u5230LR\u8f93\u5165\u56fe\u50cfx\u3002\u867d\u7136\u8fd9\u79cd\u65b9\u6cd5\u80fd\u591f\u5728\u67d0\u79cd\u5e94\u7528\u4e2d\u5c3d\u53ef\u80fd\u6062\u590d\u9891\u7387\u4fe1\u606f\uff0c\u4f46\u9ad8\u9891\u4fe1\u606f\u5374\u96be\u4ee5\u6062\u590d\uff0c\u5bb9\u6613\u51fa\u73b0\u6a21\u7cca\u60c5\u51b5\u3002\u8fd1\u51e0\u5e74\uff0c\u8bb8\u591a\u7814\u7a76\u8005\u4f7f\u7528GAN\uff0c\u7528\u4e8e\u5b66\u4e60\u9ad8\u9891\u7a7a\u95f4\u7684\u5206\u5e03\u3002\u4e22\u5931\u4e86\u9891\u8c31\u7a7a\u95f4\u7684\u9ad8\u9891\u4fe1\u606f\u3002\u56e0\u6b64\uff0c\u6587\u7ae0\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u9891\u57df\u7684\u635f\u5931\u51fd\u6570\u3002\u9996\u5148\uff0c\u5c06\u771f\u5b9e\u56fe\u50cf\u548c\u751f\u6210\u56fe\u50cf\u7ecf\u8fc7Hann window\u9884\u5904\u7406\u3002\u63a5\u7740\uff0c\u8ba1\u7b97\u5085\u91cc\u53f6\u9891\u57df\u635f\u5931\u51fd\u6570\uff0c\u5305\u62ecL1\u8303\u6570\u5ea6\u91cf\u7684\u9891\u8c31\u5dee\u5f02\uff0c\u4ee5\u53ca\u76f8\u4f4d\u89d2\u5dee\u5f02<\/p>\n\n\n\n<p>\u5bf9generate\u548creal\u8fdb\u884cfft\u53d8\u6362\uff0c\u65f6\u57df\u53d8\u6362\u5230\u9891\u57df<\/p>\n\n\n\n<p>\u53c2\u8003\u4ee3\u7801\uff1a<a href=\"https:\/\/github.com\/zzksdu\/fourierSpaceLoss\/blob\/master\/Fourier_loss.py\">https:\/\/github.com\/zzksdu\/fourierSpaceLoss\/blob\/master\/Fourier_loss.py<\/a><\/p>\n\n\n\n<p>Psnrloss\uff1a\u53c2\u8003NAFnet\u3002<\/p>\n\n\n\n<p>\u76f8\u90bb\u50cf\u7d20\u635f\u5931\uff1a<\/p>\n\n\n\n<p>\u4f7f\u7528L1loss,\u6bd4\u8f83\u76f8\u90bb\u884c\u4e4b\u95f4\u7684\u50cf\u7d20loss\uff1a<\/p>\n\n\n\n<p>Step1:<\/p>\n\n\n\n<p>\u5bf9gt\uff1a\u6c42\u76f8\u90bb\u884c\u50cf\u7d20\u7684l1\u635f\u5931\uff0c\u8bb0\u4e3a gloss\u3002<\/p>\n\n\n\n<p>Step2:<\/p>\n\n\n\n<p>\u5bf9denoise\u6c42\u76f8\u90bb\u884c\u50cf\u7d20\u7684l1\u635f\u5931\uff0c\u8bb0\u4e3a dloss\u3002<\/p>\n\n\n\n<p>Step3\uff1a\u5bf9gloss\u548cdloss\u6c42L1\u635f\u5931\u3002<\/p>\n\n\n\n<p>\u3010\u56e0\u4e3a\u56fe\u50cf\u5728\u8fdb\u884c\u9884\u5904\u7406\u65f6\u5019\u8fdb\u884c\u4e86\u884c\u5217\u4ea4\u7ec7\uff0c\u6240\u4ee5\u518d\u6c42\u6b64\u635f\u5931\u65f6\u5019\uff0c\u9700\u8981\u5148\u5bf9\u56fe\u50cf\u8fdb\u884c\u590d\u539f\uff0c\u518d\u6c42\u76f8\u90bb\u50cf\u7d20\u635f\u5931\u3011<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"645\" height=\"448\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-39.png\" alt=\"\" class=\"wp-image-4904\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-39.png 645w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-39-300x208.png 300w\" sizes=\"(max-width: 645px) 100vw, 645px\" \/><\/figure>\n\n\n\n<p>3\u3001\u4f18\u5316\u51fd\u6570 SGD or Adam<\/p>\n\n\n\n<p>SGD\u867d\u7136\u8bad\u7ec3\u65f6\u95f4\u66f4\u957f\uff0c\u5bb9\u6613\u9677\u5165\u978d\u70b9\uff0c\u4f46\u662f\u5728\u597d\u7684\u521d\u59cb\u5316\u548c\u5b66\u4e60\u7387\u8c03\u5ea6\u65b9\u6848\u7684\u60c5\u51b5\u4e0b\uff0c\u7ed3\u679c\u66f4\u53ef\u9760\u3002SGD\u73b0\u5728\u540e\u671f\u8c03\u4f18\u65f6\u8fd8\u662f\u7ecf\u5e38\u4f7f\u7528\u5230\uff0c\u4f46SGD\u7684\u95ee\u9898\u662f\u524d\u671f\u6536\u655b\u901f\u5ea6\u6162\u3002SGD\u524d\u671f\u6536\u655b\u6162\u7684\u539f\u56e0\uff1a SGD\u5728\u66f4\u65b0\u53c2\u6570\u65f6\u5bf9\u5404\u4e2a\u7ef4\u5ea6\u4e0a\u68af\u5ea6\u7684\u653e\u7f29\u662f\u4e00\u81f4\u7684\uff0c\u5e76\u4e14\u5728\u8bad\u7ec3\u6570\u636e\u5206\u5e03\u6781\u4e0d\u5747\u8861\u65f6\u8bad\u7ec3\u6548\u679c\u5f88\u5dee\u3002\u800c\u56e0\u4e3a\u6536\u655b\u6162\u7684\u95ee\u9898\u5e94\u8fd0\u800c\u751f\u7684\u81ea\u9002\u5e94\u4f18\u5316\u7b97\u6cd5Adam\u3001AdaGrad\u3001RMSprop \u7b49\uff0c\u4f46\u8fd9\u4e9b\u81ea\u9002\u5e94\u7684\u4f18\u5316\u7b97\u6cd5\u6cdb\u5316\u80fd\u529b\u53ef\u80fd\u6bd4\u975e\u81ea\u9002\u5e94\u65b9\u6cd5\u66f4\u5dee\uff0c\u867d\u7136\u53ef\u4ee5\u5728\u8bad\u7ec3\u521d\u59cb\u9636\u6bb5\u5c55\u73b0\u51fa\u5feb\u901f\u7684\u6536\u655b\u901f\u5ea6\uff0c\u4f46\u5176\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0\u5374\u4f1a\u5f88\u5feb\u9677\u5165\u505c\u6ede\uff0c\u5e76\u6700\u7ec8\u88ab SGD \u8d85\u8fc7\u3002 \u5b9e\u9645\u4e0a\uff0c\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u65b9\u9762\u7684\u4e00\u4e9b\u6700\u65b0\u7684\u5de5\u4f5c\u4e2dSGD\uff08\u6216\u52a8\u91cf\uff09\u88ab\u9009\u4e3a\u4f18\u5316\u5668\uff0c\u5176\u4e2d\u8fd9\u4e9b\u5b9e\u4f8b\u4e2dSGD \u786e\u5b9e\u6bd4\u81ea\u9002\u5e94\u65b9\u6cd5\u8868\u73b0\u66f4\u597d\u3002<\/p>\n\n\n\n<p>\u4e3b\u6d41\u8ba4\u4e3a\uff1aAdam\u7b49\u81ea\u9002\u5e94\u5b66\u4e60\u7387\u7b97\u6cd5\u5bf9\u4e8e\u7a00\u758f\u6570\u636e\u5177\u6709\u4f18\u52bf\uff0c\u4e14\u6536\u655b\u901f\u5ea6\u5f88\u5feb\uff1b\u4f46\u7cbe\u8c03\u53c2\u6570\u7684SGD\uff08+Momentum\uff09\u5f80\u5f80\u80fd\u591f\u53d6\u5f97\u66f4\u597d\u7684\u6700\u7ec8\u7ed3\u679c\u3002<\/p>\n\n\n\n<p>Improving Generalization Performance by Switching from Adam to SGD \u63d0\u51fa\u4e86Adam+SGD \u7ec4\u5408\u7b56\u7565\u3002\u524d\u671f\u7528Adam\uff0c\u4eab\u53d7Adam\u5feb\u901f\u6536\u655b\u7684\u4f18\u52bf\uff1b\u540e\u671f\u5207\u6362\u5230SGD\uff0c\u6162\u6162\u5bfb\u627e\u6700\u4f18\u89e3\u3002\u8fd9\u4e00\u65b9\u6cd5\u4ee5\u524d\u4e5f\u88ab\u7814\u7a76\u8005\u4eec\u7528\u5230\uff0c\u4e0d\u8fc7\u4e3b\u8981\u662f\u6839\u636e\u7ecf\u9a8c\u6765\u9009\u62e9\u5207\u6362\u7684\u65f6\u673a\u548c\u5207\u6362\u540e\u7684\u5b66\u4e60\u7387\u3002\u8fd9\u7bc7\u6587\u7ae0\u628a\u8fd9\u4e00\u5207\u6362\u8fc7\u7a0b\u50bb\u74dc\u5316\uff0c\u7ed9\u51fa\u4e86\u5207\u6362SGD\u7684\u65f6\u673a\u9009\u62e9\u65b9\u6cd5\uff0c\u4ee5\u53ca\u5b66\u4e60\u7387\u7684\u8ba1\u7b97\u65b9\u6cd5\uff0c\u6548\u679c\u770b\u8d77\u6765\u4e5f\u4e0d\u9519\u3002<\/p>\n\n\n\n<p>torch.optim.lr_scheduler.StepLR\uff1a\u8fd9\u662f\u6bd4\u8f83\u5e38\u7528\u7684\u7b49\u95f4\u9694\u52a8\u6001\u8c03\u6574\u65b9\u6cd5\uff0c\u8be5\u65b9\u6cd5\u7684\u539f\u7406\u4e3a\uff1a\u6bcf\u9694step_size\u4e2aepoch\u5c31\u5bf9\u6bcf\u4e00\u53c2\u6570\u7ec4\u7684\u5b66\u4e60\u7387\u6309gamma\u53c2\u6570\u8fdb\u884c\u4e00\u6b21\u8870\u51cf\u3002<\/p>\n\n\n\n<p>3\u3001\u8bad\u7ec3\u7ed3\u679c\uff1a<\/p>\n\n\n\n<p>Batch Size=1\uff0c\u68af\u5ea6\u53d8\u6765\u53d8\u53bb\uff0c\u975e\u5e38\u4e0d\u51c6\u786e\uff0c\u7f51\u7edc\u5f88\u96be\u6536\u655b\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"909\" height=\"295\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-40.png\" alt=\"\" class=\"wp-image-4905\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-40.png 909w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-40-300x97.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-40-768x249.png 768w\" sizes=\"(max-width: 909px) 100vw, 909px\" \/><\/figure>\n\n\n\n<p>Figure 1 loss\u635f\u5931\u51fd\u6570\u503c<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"909\" height=\"185\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-41.png\" alt=\"\" class=\"wp-image-4906\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-41.png 909w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-41-300x61.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-41-768x156.png 768w\" sizes=\"(max-width: 909px) 100vw, 909px\" \/><\/figure>\n\n\n\n<p>Figure 2 &nbsp;\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684psnr\u6d4b\u8bd5\u503c<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"792\" height=\"478\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-42.png\" alt=\"\" class=\"wp-image-4907\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-42.png 792w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-42-300x181.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-42-768x464.png 768w\" sizes=\"(max-width: 792px) 100vw, 792px\" \/><\/figure>\n\n\n\n<p>Figure 3 &nbsp;&nbsp;\u8bad\u7ec3\u8fc7\u7a0bSSIM \u6d4b\u8bd5\u503c<\/p>\n\n\n\n<p>4\u3001\u6570\u636e\u96c6\u5904\u7406<\/p>\n\n\n\n<p>\u5bf9\u6570\u636e\u8fdb\u884c\u52a0\u566a\u5904\u7406\u3001\u6570\u636e\u968f\u673a\u7ffb\u8f6c\u7b49\u9884\u5904\u7406\u3002<\/p>\n\n\n\n<p>\u56e0\u4e3a\u663e\u5b58\u6709\u9650\uff0c\u6240\u4ee5\u5c06img\uff084\uff0c1736,2312\uff09\u88c1\u526a\u4e3a 1736\/4, 2312\/4\u7684\u5927\u5c0f\u9001\u5165\u7f51\u7edc\u4e2d\u8fdb\u884c\u8bad\u7ec3\u3002\u5728\u8fdb\u884c\u9a8c\u8bc1\/\u751f\u6210\u53bb\u566a\u56fe\u7247\u65f6\uff0c\u540c\u6837\u88c1\u526a\u566a\u58f0\u56fe\u7247\u5206\u6279\u9001\u5165\u7f51\u7edc\uff0c\u6ce8\u610f\uff0c\u88c1\u526a\u9700\u8981\u591a\u88c1\u526a20\u4e2atensor\uff0c\u7136\u540e\u62fc\u63a5\u6210\u5b8c\u6574\u56fe\u7247\u5e76\u5199\u5165\u6587\u4ef6\uff0c\u8fd9\u6837\u62fc\u63a5\u6210\u7684\u56fe\u7247\u4e0d\u4f1a\u6709\u5206\u5272\u7ebf \u3002<\/p>\n\n\n\n<p>[\u52a0\u566a\u5904\u7406\u6548\u679c\u4e0d\u597d\uff0c\u6211\u4f7f\u7528\u7684\u566a\u58f0\u6b63\u5219\u9879\u662ftorch\u6b63\u6001\u5206\u5e03\uff0c\u539f\u672c\u4efb\u52a1\u5c31\u662f\u53bb\u566a\uff0craw\u57df\u566a\u58f0\u7684\u5206\u5e03\u5e94\u8be5\u8ddf\u6b63\u6001\u5206\u5e03\u4e0d\u8d34\u5408\uff0c\u589e\u52a0\u566a\u58f0\u540e\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u6548\u679c\u53d8\u5dee\u3002<\/p>\n\n\n\n<p>\u6570\u636e\u7ffb\u8f6c\uff1a\u8fd9\u91cc\u9700\u8981\u6ce8\u610flabel\u548cnoise\u5e94\u8be5\u4f7f\u7528\u76f8\u540c\u7684seed\u3002<\/p>\n\n\n\n<p>\u56e0\u4e3a\u663e\u5b58\u6709\u9650\uff0c\u9700\u8981\u88c1\u526a\u56fe\u50cf\uff0c\u8fd9\u91cc\u6211\u8ba4\u4e3a\u5982\u679c\u53ea\u662f\u7b80\u5355\u7684\u88c1\u526a\u56fe\u50cf\uff0c\u4f1a\u5bfc\u81f4\u88c1\u526a\u524d\u540e\u7684\u76f8\u90bb\u50cf\u7d20\u4fe1\u606f\u635f\u5931\uff0c\u56e0\u6b64\u6211\u501f\u9274\u4e86unet\uff1aOverlap-tile \u91cd\u53e0\u5207\u7247\u7684\u601d\u60f3\uff0c\u540c\u65f6\uff0c\u5728\u751f\u6210test\u56fe\u50cf\u65f6\u5019\uff0c\u4e5f\u8981\u5bf9\u5176\u8fdb\u884c\u62fc\u63a5\u3002]<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"689\" height=\"347\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-43.png\" alt=\"\" class=\"wp-image-4908\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-43.png 689w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-43-300x151.png 300w\" sizes=\"(max-width: 689px) 100vw, 689px\" \/><\/figure>\n\n\n\n<p>5\u3001\u4f7f\u7528\u52a0\u8f7d\u6a21\u578b\uff1a<\/p>\n\n\n\n<p>from network import NFnet3<\/p>\n\n\n\n<p>net = NFnet3()<\/p>\n\n\n\n<p>net.load_state_dict(torch.load(modelpath))<\/p>\n\n\n\n<p>6\u3001test\u6570\u636e\u96c6\u53bb\u566a\u7ed3\u679c\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"914\" height=\"462\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-44.png\" alt=\"\" class=\"wp-image-4909\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-44.png 914w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-44-300x152.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-44-768x388.png 768w\" sizes=\"(max-width: 914px) 100vw, 914px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"754\" height=\"711\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-45.png\" alt=\"\" class=\"wp-image-4910\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-45.png 754w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-45-300x283.png 300w\" sizes=\"(max-width: 754px) 100vw, 754px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"761\" height=\"401\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-46.png\" alt=\"\" class=\"wp-image-4911\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-46.png 761w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-46-300x158.png 300w\" sizes=\"(max-width: 761px) 100vw, 761px\" \/><figcaption>Figure 6 \u539f\u59cb\u566a\u58f0\u56fe\u50cf<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"761\" height=\"402\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-47.png\" alt=\"\" class=\"wp-image-4912\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-47.png 761w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-47-300x158.png 300w\" sizes=\"(max-width: 761px) 100vw, 761px\" \/><figcaption>Figure 7 \u53bb\u566a\u540e\u56fe\u50cf<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>\u53bb\u566a\u7ed3\u679c\u601d\u8003\uff1a<\/p>\n\n\n\n<p>\u5bf9\u4e8e\u566a\u58f0\u6bd4\u8f83\u5c0f\u7684\u56fe\u50cf\uff0c\u53bb\u566a\u6548\u679c\u6bd4\u8f83\u597d\uff0c\u800c\u4e14\u4e0d\u4f1a\u7834\u574f\u539f\u6709\u56fe\u50cf\u7684\u7ed3\u6784\uff0c\u4f46\u5bf9\u4e8e\u566a\u58f0\u7279\u522b\u5927\u7684\u56fe\u50cf\uff1a\u7eb9\u7406\u6548\u679c\u4e0d\u662f\u5f88\u597d\uff0c\u4e00\u4e9b\u7ec6\u8282\u5904\u7406\u7684\u4e0d\u592a\u597d\u3002<\/p>\n\n\n\n<p>\u6b64\u5916\uff0c\u6211\u8ba4\u4e3a\uff0c\u5982\u679c\u8f93\u5165\u88c1\u526a\u540e\u7684\u56fe\u50cf\u80fd\u5728\u5927\u4e00\u4e9b\uff0c\u6548\u679c\u5e94\u8be5\u4f1a\u597d\u4e00\u4e9b\u3002\u6216\u8005\u73b0\u5728\u5c0f\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u8bad\u7ec3\uff0c\u5728\u4f7f\u7528\u5927\u56fe\u50cf\u8fdb\u884c\u5fae\u8c03\uff0c\u4f1a\u597d\u4e00\u4e9b\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"846\" height=\"589\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-48.png\" alt=\"\" class=\"wp-image-4914\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-48.png 846w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-48-300x209.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-48-768x535.png 768w\" sizes=\"(max-width: 846px) 100vw, 846px\" \/><\/figure>\n\n\n\n<p>\u53c2\u8003\u8bba\u6587\uff1a<\/p>\n\n\n\n<p>[1] Chen L, Chu X, Zhang X, et al. Simple Baselines for Image Restoration[J]. arXiv preprint arXiv:2204.04676, 2022.<\/p>\n\n\n\n<p>[2] Huang H, Lin L, Tong R, et al. Unet 3+: A full-scale connected unet for medical image segmentation[C]\/\/ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020: 1055-1059.<\/p>\n\n\n\n<p>[3] Wang Y, Huang H, Xu Q, et al. Practical deep raw image denoising on mobile devices[C]\/\/European Conference on Computer Vision. Springer, Cham, 2020: 1-16.<\/p>\n\n\n\n<p>[4] Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprintarXiv:1607.06450 (2016)<\/p>\n\n\n\n<p>[5] Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao,W.: Pre-trained image processing transformer. In: Proceedings of the IEEE\/CVFConference on Computer Vision and Pattern Recognition. pp. 12299\u201312310 (2021)<\/p>\n\n\n\n<p>[6] Cheng, S., Wang, Y., Huang, H., Liu, D., Fan, H., Liu, S.: Nbnet: Noise basis learning for image denoising with subspace projection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4896\u20134906 (2021)<\/p>\n\n\n\n<p>[7] Language Modeling with Gated Convolutional Networks<\/p>\n\n\n\n<p class=\"has-light-pink-background-color has-background\"><strong>\u66f4\u65b0\uff1a\u6700\u8fd1Swin Transformer\u7684\u63d0\u51fa\uff0c\u5c31\u6709\u4eba\u5229\u7528\u8be5\u7ed3\u6784\u548cunet\uff0c\u5b9e\u73b0\u4e86\u56fe\u50cf\u53bb\u566a\uff1a<\/strong><\/p>\n\n\n\n<h5>SUNet: Swin Transformer UNet for Image Denoising<\/h5>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2202.14009\">https:\/\/arxiv.org\/abs\/2202.14009<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"952\" height=\"436\" src=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-49.png\" alt=\"\" class=\"wp-image-4915\" srcset=\"http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-49.png 952w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-49-300x137.png 300w, http:\/\/139.9.1.231\/wp-content\/uploads\/2022\/07\/image-49-768x352.png 768w\" sizes=\"(max-width: 952px) 100vw, 952px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>\u4e2d\u5174\u6367\u6708\u5927\u8d5b \uff1ahttps:\/\/zte.hina.com\/zte\/index \u56fe\u50cf\u53bb\u566a\u8d5b\u9898\u80cc\u666f\u56fe\u50cf\u53bb\u566a\u662f\u673a\u5668\u89c6 &hellip; <a href=\"http:\/\/139.9.1.231\/index.php\/2022\/07\/20\/deniose\/\" class=\"more-link\">\u7ee7\u7eed\u9605\u8bfb<span class=\"screen-reader-text\">\u4e2d\u5174\u5927\u8d5b<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[18],"tags":[],"_links":{"self":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/4677"}],"collection":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/comments?post=4677"}],"version-history":[{"count":16,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/4677\/revisions"}],"predecessor-version":[{"id":6417,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/4677\/revisions\/6417"}],"wp:attachment":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/media?parent=4677"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/categories?post=4677"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/tags?post=4677"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}