{"id":40496,"date":"2025-05-29T09:37:56","date_gmt":"2025-05-29T01:37:56","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/40496.html"},"modified":"2025-05-29T09:37:56","modified_gmt":"2025-05-29T01:37:56","slug":"opengan%ef%bc%9a%e5%9f%ba%e4%ba%8e%e5%bc%80%e6%94%be%e6%95%b0%e6%8d%ae%e7%94%9f%e6%88%90%e7%9a%84%e5%bc%80%e6%94%be%e9%9b%86%e8%af%86%e5%88%ab","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/40496.html","title":{"rendered":"OpenGAN\uff1a\u57fa\u4e8e\u5f00\u653e\u6570\u636e\u751f\u6210\u7684\u5f00\u653e\u96c6\u8bc6\u522b"},"content":{"rendered":"<h2 style=\"text-align:left\">\u7b80\u4ecb<\/h2>\n<p style=\"margin-left:0;margin-right:0;text-align:left\">\u7b80\u4ecb&#xff1a;\u8fd9\u6b21\u5b66\u4e60\u7684OpenGAN\u4e3b\u8981\u5b66\u4e60\u4e00\u4e2a\u601d\u8def&#xff0c;\u8df3\u51fa\u4f20\u7edfGAN\u5bf9\u4e8e\u5224\u65ad\u771f\u5047\u7684\u8bc6\u522b\u5230\u5224\u65ad\u662f\u5df2\u77e5\u79cd\u7c7b\u8fd8\u662f\u672a\u77e5\u79cd\u7c7b\u3002\u91cd\u70b9\u5185\u5bb9\u4e0d\u5728\u4e8e\u4ee3\u7801\u800c\u662f\u601d\u8def&#xff0c;\u4f1a\u7b80\u8981\u7ed9\u51fa\u4e00\u4e2a\u8bbe\u8ba1\u7684\u4ee3\u7801\u3002<\/p>\n<p style=\"margin-left:0;margin-right:0;text-align:left\">\u8bba\u6587\u9898\u76ee&#xff1a;OpenGAN: Open-Set Recognition via Open Data Generation&#xff08;\u57fa\u4e8e\u5f00\u653e\u6570\u636e\u751f\u6210\u7684\u5f00\u653e\u96c6\u8bc6\u522b&#xff09;<\/p>\n<p style=\"margin-left:0;margin-right:0;text-align:left\">\u671f\u520a&#xff1a;IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE&#xff08;if&#061;20.8,\u8d85\u7ea7Top&#xff09;<\/p>\n<p style=\"margin-left:0px;margin-right:0px;text-align:justify\">\u6458\u8981&#xff1a;\u73b0\u5b9e\u4e16\u754c\u7684\u673a\u5668\u5b66\u4e60\u7cfb\u7edf\u9700\u8981\u5206\u6790\u53ef\u80fd\u4e0e\u8bad\u7ec3\u6570\u636e\u4e0d\u540c\u7684\u6d4b\u8bd5\u6570\u636e\u3002 \u5728K-way\u5206\u7c7b\u4e2d&#xff0c;\u8fd9\u88ab\u6e05\u6670\u5730\u8868\u8ff0\u4e3a\u5f00\u96c6\u8bc6\u522b&#xff0c;\u5176\u6838\u5fc3\u662f\u533a\u5206K\u4e2a\u95ed\u96c6\u7c7b\u4e4b\u5916\u7684\u5f00\u96c6\u6570\u636e\u7684\u80fd\u529b\u3002 \u5f00\u96c6\u5224\u522b\u7684\u4e24\u4e2a\u6982\u5ff5\u662f&#xff1a;1)\u901a\u8fc7\u5229\u7528\u4e00\u4e9b\u79bb\u7fa4\u6570\u636e\u4f5c\u4e3a\u5f00\u96c6\u5224\u522b\u5b66\u4e60\u5f00-\u95ed\u4e8c\u5143\u5224\u522b\u5668&#xff1b; 2)\u5229\u7528GAN\u7684\u5224\u522b\u5668\u4f5c\u4e3a\u5f00\u96c6\u4f3c\u7136\u51fd\u6570&#xff0c;\u5bf9\u95ed\u96c6\u6570\u636e\u5206\u5e03\u8fdb\u884c\u65e0\u76d1\u7763\u5b66\u4e60\u3002 \u7136\u800c&#xff0c;\u7531\u4e8e\u5bf9\u8bad\u7ec3\u5f02\u5e38\u503c\u7684\u8fc7\u5ea6\u62df\u5408&#xff0c;\u524d\u8005\u5bf9\u5404\u79cd\u5f00\u653e\u6d4b\u8bd5\u6570\u636e\u7684\u6cdb\u5316\u6548\u679c\u8f83\u5dee&#xff0c;\u800c\u8bad\u7ec3\u5f02\u5e38\u503c\u4e0d\u592a\u53ef\u80fd\u8be6\u5c3d\u5730\u8de8\u8d8a\u5f00\u653e\u4e16\u754c\u3002 \u540e\u8005\u4e0d\u80fd\u5f88\u597d\u5730\u5de5\u4f5c&#xff0c;\u53ef\u80fd\u662f\u7531\u4e8egan\u7684\u8bad\u7ec3\u4e0d\u7a33\u5b9a\u3002 \u5728\u4e0a\u8ff0\u7684\u6fc0\u52b1\u4e0b&#xff0c;\u6211\u4eec\u63d0\u51fa\u4e86OpenGAN&#xff0c;\u5b83\u901a\u8fc7\u5c06\u6bcf\u79cd\u65b9\u6cd5\u4e0e\u51e0\u4e2a\u6280\u672f\u89c1\u89e3\u76f8\u7ed3\u5408\u6765\u89e3\u51b3\u6bcf\u79cd\u65b9\u6cd5\u7684\u5c40\u9650\u6027\u3002 \u9996\u5148&#xff0c;\u6211\u4eec\u8bc1\u660e\u4e86\u5728\u4e00\u4e9b\u771f\u5b9e\u7684\u79bb\u7fa4\u6570\u636e\u4e0a\u7cbe\u5fc3\u9009\u62e9\u7684gan\u9274\u522b\u5668\u5df2\u7ecf\u8fbe\u5230\u4e86\u6700\u5148\u8fdb\u7684\u6c34\u5e73\u3002 \u5176\u6b21&#xff0c;\u6211\u4eec\u7528\u5bf9\u6297\u5408\u6210\u7684\u201c\u5047\u201d\u6570\u636e\u589e\u5f3a\u771f\u5b9e\u5f00\u653e\u8bad\u7ec3\u6837\u672c\u7684\u53ef\u7528\u96c6\u3002 \u7b2c\u4e09&#xff0c;\u4e5f\u662f\u6700\u91cd\u8981\u7684&#xff0c;\u6211\u4eec\u5728\u5c01\u95ed\u4e16\u754cK-way\u7f51\u7edc\u8ba1\u7b97\u7684\u7279\u5f81\u4e0a\u5efa\u7acb\u4e86\u9274\u522b\u5668\u3002 \u8fd9\u4f7f\u5f97OpenGAN\u53ef\u4ee5\u901a\u8fc7\u5efa\u7acb\u5728\u73b0\u6709K-way\u7f51\u7edc\u4e4b\u4e0a\u7684\u8f7b\u91cf\u7ea7\u9274\u522b\u5668\u5934\u6765\u5b9e\u73b0\u3002 \u5927\u91cf\u7684\u5b9e\u9a8c\u8868\u660e&#xff0c;OpenGAN\u663e\u8457\u4f18\u4e8e\u5148\u524d\u7684\u5f00\u96c6\u65b9\u6cd5\u3002<\/p>\n<h2 style=\"text-align:left\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"285\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/05\/20250529013754-6837ba72d3cfc.png\" width=\"1563\" \/><\/h2>\n<h3>\u95ee\u9898\u80cc\u666f\u7684\u5177\u4f53\u4f8b\u5b50<\/h3>\n<p>\u60f3\u8c61\u4f60\u8981\u8bad\u7ec3\u4e00\u4e2a\u81ea\u52a8\u9a7e\u9a76\u6c7d\u8f66\u7684\u89c6\u89c9\u7cfb\u7edf&#xff1a;<\/p>\n<ul>\n<li>\u8bad\u7ec3\u6570\u636e&#xff1a;\u6c7d\u8f66\u3001\u884c\u4eba\u3001\u7ea2\u7eff\u706f\u3001\u5efa\u7b51\u7269\u7b4919\u4e2a\u7c7b\u522b<\/li>\n<li>\u73b0\u5b9e\u95ee\u9898&#xff1a;\u8def\u4e0a\u7a81\u7136\u51fa\u73b0\u5a74\u513f\u8f66\u3001\u8857\u5934\u5c0f\u644a\u7b49\u8bad\u7ec3\u65f6\u6ca1\u89c1\u8fc7\u7684\u7269\u4f53<\/li>\n<li>\u5371\u9669\u540e\u679c&#xff1a;\u7cfb\u7edf\u53ef\u80fd\u628a\u5a74\u513f\u8f66\u9519\u8bef\u8bc6\u522b\u4e3a&#034;\u6469\u6258\u8f66&#034;&#xff0c;\u5bfc\u81f4\u4e0d\u5f53\u7684\u907f\u8ba9\u7b56\u7565<\/li>\n<\/ul>\n<h3>OpenGAN\u7684\u5177\u4f53\u64cd\u4f5c\u6d41\u7a0b<\/h3>\n<h4>\u7b2c\u4e00\u6b65&#xff1a;\u51c6\u5907\u57fa\u7840\u8bbe\u65bd<\/h4>\n<p>1. \u5df2\u6709\u4e00\u4e2a\u8bad\u7ec3\u597d\u7684K\u7c7b\u5206\u7c7b\u5668&#xff08;\u6bd4\u5982\u8bc6\u522b19\u79cd\u4ea4\u901a\u573a\u666f\u7269\u4f53&#xff09; 2. \u4ece\u8fd9\u4e2a\u5206\u7c7b\u5668\u7684\u5012\u6570\u7b2c\u4e8c\u5c42\u63d0\u53d6\u7279\u5f81&#xff08;\u4e0d\u662f\u76f4\u63a5\u7528\u539f\u59cb\u56fe\u7247&#xff09; 3. \u6536\u96c6\u5c11\u91cf&#034;\u5176\u4ed6&#034;\u7c7b\u522b\u7684\u6570\u636e\u4f5c\u4e3a\u5df2\u77e5\u7684\u5f02\u5e38\u6837\u672c<\/p>\n<p>\u4e3a\u4ec0\u4e48\u7528\u7279\u5f81\u800c\u4e0d\u7528\u50cf\u7d20&#xff1f;<\/p>\n<ul>\n<li>\u539f\u59cb\u56fe\u7247&#xff1a;1024\u00d72048\u00d73 &#061; 600\u4e07\u7ef4\u5ea6&#xff0c;\u592a\u590d\u6742<\/li>\n<li>\u63d0\u53d6\u7684\u7279\u5f81&#xff1a;\u53ef\u80fd\u53ea\u6709512\u7ef4&#xff0c;\u5305\u542b\u4e86\u9ad8\u7ea7\u8bed\u4e49\u4fe1\u606f<\/li>\n<li>\u5c31\u50cf\u4eba\u7c7b\u8bc6\u522b\u7269\u4f53\u65f6\u5173\u6ce8\u7684\u662f\u5f62\u72b6\u3001\u7eb9\u7406&#xff0c;\u800c\u4e0d\u662f\u6bcf\u4e2a\u50cf\u7d20\u70b9<\/li>\n<\/ul>\n<h4>\u7b2c\u4e8c\u6b65&#xff1a;\u6784\u5efaOpenGAN\u67b6\u6784<\/h4>\n<p>OpenGAN\u5305\u542b\u4e24\u4e2a\u6838\u5fc3\u7ec4\u4ef6&#xff1a;<\/p>\n<p>\u5224\u522b\u5668D&#xff08;Discriminator&#xff09;&#xff1a;<\/p>\n<ul>\n<li>\u8f93\u5165&#xff1a;\u7279\u5f81\u5411\u91cf<\/li>\n<li>\u8f93\u51fa&#xff1a;\u8be5\u7279\u5f81\u5c5e\u4e8e&#034;\u5df2\u77e5\u7c7b\u522b&#034;\u7684\u6982\u7387<\/li>\n<li>\u4f5c\u7528&#xff1a;\u533a\u5206&#034;\u5df2\u77e5&#034;vs&#034;\u672a\u77e5&#034;<\/li>\n<\/ul>\n<p>\u751f\u6210\u5668G&#xff08;Generator&#xff09;&#xff1a;<\/p>\n<ul>\n<li>\u8f93\u5165&#xff1a;\u968f\u673a\u566a\u58f0<\/li>\n<li>\u8f93\u51fa&#xff1a;\u5047\u7684&#034;\u672a\u77e5\u7c7b\u522b&#034;\u7279\u5f81<\/li>\n<li>\u4f5c\u7528&#xff1a;\u751f\u6210\u66f4\u591a\u6837\u7684&#034;\u672a\u77e5&#034;\u6837\u672c\u6765\u8bad\u7ec3\u5224\u522b\u5668<\/li>\n<\/ul>\n<h4>\u7b2c\u4e09\u6b65&#xff1a;\u5bf9\u6297\u8bad\u7ec3\u8fc7\u7a0b<\/h4>\n<p>\u8fd9\u662f\u6838\u5fc3&#xff01;OpenGAN\u4f7f\u7528\u4e09\u79cd\u6570\u636e\u540c\u65f6\u8bad\u7ec3&#xff1a;<\/p>\n<p># \u4f2a\u4ee3\u7801\u5c55\u793a\u8bad\u7ec3\u8fc7\u7a0b<br \/>\nfor epoch in training:<br \/>\n    # 1. \u771f\u5b9e\u7684\u5df2\u77e5\u7c7b\u522b\u6570\u636e<br \/>\n    real_closed_features &#061; extract_features(known_class_images)<\/p>\n<p>    # 2. \u771f\u5b9e\u7684\u672a\u77e5\u7c7b\u522b\u6570\u636e&#xff08;\u5c11\u91cf&#xff09;<br \/>\n    real_open_features &#061; extract_features(outlier_images)<\/p>\n<p>    # 3. \u751f\u6210\u5668\u521b\u9020\u7684\u5047\u672a\u77e5\u6570\u636e<br \/>\n    noise &#061; random_noise()<br \/>\n    fake_open_features &#061; Generator(noise)<\/p>\n<p>    # \u8bad\u7ec3\u5224\u522b\u5668&#xff1a;\u8ba9\u5b83\u5b66\u4f1a\u533a\u5206\u5df2\u77e5\u548c\u672a\u77e5<br \/>\n    d_loss &#061; &#8211; log(D(real_closed_features))           # \u5df2\u77e5\u7c7b\u522b\u5e94\u8be5\u5f97\u9ad8\u5206<br \/>\n             &#8211; \u03bb_o * log(1-D(real_open_features))     # \u771f\u5b9e\u672a\u77e5\u5e94\u8be5\u5f97\u4f4e\u5206<br \/>\n             &#8211; \u03bb_G * log(1-D(fake_open_features))     # \u751f\u6210\u672a\u77e5\u5e94\u8be5\u5f97\u4f4e\u5206<\/p>\n<p>    # \u8bad\u7ec3\u751f\u6210\u5668&#xff1a;\u8ba9\u5b83\u751f\u6210\u80fd\u9a97\u8fc7\u5224\u522b\u5668\u7684&#034;\u672a\u77e5&#034;\u7279\u5f81<br \/>\n    g_loss &#061; &#8211; log(1-D(Generator(noise)))  # \u751f\u6210\u7684\u7279\u5f81\u8981\u80fd\u9a97\u8fc7\u5224\u522b\u5668 <\/p>\n<p>\u5173\u952e\u53c2\u6570\u89e3\u91ca&#xff1a;<\/p>\n<ul>\n<li>\u03bb_o&#xff1a;\u63a7\u5236\u771f\u5b9e\u5f02\u5e38\u6570\u636e\u7684\u91cd\u8981\u6027<\/li>\n<li>\u03bb_G&#xff1a;\u63a7\u5236\u751f\u6210\u5047\u6570\u636e\u7684\u91cd\u8981\u6027<\/li>\n<li>\u5f53\u03bb_o&#061;0\u65f6&#xff0c;\u5c31\u662fOpenGAN-0&#xff08;\u4e0d\u4f7f\u7528\u771f\u5b9e\u5f02\u5e38\u6570\u636e&#xff09;<\/li>\n<\/ul>\n<h4>\u7b2c\u56db\u6b65&#xff1a;\u6a21\u578b\u9009\u62e9\u7684\u5de7\u5999\u65b9\u6cd5<\/h4>\n<p>\u4f20\u7edfGAN\u7684\u95ee\u9898&#xff1a;<\/p>\n<ul>\n<li>\u8bad\u7ec3\u5230\u6700\u540e&#xff0c;\u751f\u6210\u5668\u592a\u5f3a\u4e86&#xff0c;\u5224\u522b\u5668\u5206\u4e0d\u51fa\u771f\u5047<\/li>\n<li>\u5224\u522b\u5668\u5931\u53bb\u4e86\u533a\u5206&#034;\u5df2\u77e5&#034;\u548c&#034;\u672a\u77e5&#034;\u7684\u80fd\u529b<\/li>\n<\/ul>\n<p>OpenGAN\u7684\u89e3\u51b3\u65b9\u6848&#xff1a;<\/p>\n<li>\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4fdd\u5b58\u591a\u4e2a\u5224\u522b\u5668\u5feb\u7167<\/li>\n<li>\u7528\u5c11\u91cf\u771f\u5b9e\u7684\u5f02\u5e38\u6570\u636e\u4f5c\u4e3a\u9a8c\u8bc1\u96c6<\/li>\n<li>\u9009\u62e9\u5728\u9a8c\u8bc1\u96c6\u4e0a\u8868\u73b0\u6700\u597d\u7684\u5224\u522b\u5668<\/li>\n<p># \u6a21\u578b\u9009\u62e9\u8fc7\u7a0b<br \/>\nbest_auroc &#061; 0<br \/>\nbest_discriminator &#061; None<\/p>\n<p>for checkpoint in training_checkpoints:<br \/>\n    discriminator &#061; load_checkpoint(checkpoint)<br \/>\n    auroc &#061; evaluate_on_validation_set(discriminator, validation_outliers)<\/p>\n<p>    if auroc &gt; best_auroc:<br \/>\n        best_auroc &#061; auroc<br \/>\n        best_discriminator &#061; discriminator<\/p>\n<p># \u4f7f\u7528\u6700\u4f73\u5224\u522b\u5668\u8fdb\u884c\u6700\u7ec8\u9884\u6d4b <\/p>\n<h4>\u7b2c\u4e94\u6b65&#xff1a;\u5b9e\u9645\u5e94\u7528<\/h4>\n<p>\u5728\u6d4b\u8bd5\u65f6&#xff1a;<\/p>\n<p>def predict_open_set(test_image):<br \/>\n    # 1. \u63d0\u53d6\u7279\u5f81<br \/>\n    features &#061; pretrained_classifier.extract_features(test_image)<\/p>\n<p>    # 2. \u7528OpenGAN\u5224\u522b\u5668\u6253\u5206<br \/>\n    confidence &#061; best_discriminator(features)<\/p>\n<p>    # 3. \u505a\u51b3\u7b56<br \/>\n    if confidence &gt; threshold:<br \/>\n        # \u8fdb\u884c\u6b63\u5e38\u7684K\u7c7b\u5206\u7c7b<br \/>\n        class_prediction &#061; pretrained_classifier(test_image)<br \/>\n        return class_prediction<br \/>\n    else:<br \/>\n        return &#034;UNKNOWN_OBJECT&#034;  # \u672a\u77e5\u7269\u4f53 <\/p>\n<p style=\"margin-left:.0001pt;margin-right:0;text-align:justify\">\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb1k\u6b21\uff0c\u70b9\u8d5e24\u6b21\uff0c\u6536\u85cf13\u6b21\u3002\u8fd9\u6b21\u5b66\u4e60\u7684OpenGAN\u4e3b\u8981\u5b66\u4e60\u4e00\u4e2a\u601d\u8def\uff0c\u8df3\u51fa\u4f20\u7edfGAN\u5bf9\u4e8e\u5224\u65ad\u771f\u5047\u7684\u8bc6\u522b\u5230\u5224\u65ad\u662f\u5df2\u77e5\u79cd\u7c7b\u8fd8\u662f\u672a\u77e5\u79cd\u7c7b\u3002\u91cd\u70b9\u5185\u5bb9\u4e0d\u5728\u4e8e\u4ee3\u7801\u800c\u662f\u601d\u8def\uff0c\u4f1a\u7b80\u8981\u7ed9\u51fa\u4e00\u4e2a\u8bbe\u8ba1\u7684\u4ee3\u7801\u3002<\/p>\n","protected":false},"author":2,"featured_media":40495,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3710,152,50,207,86,1821,427,3548],"topic":[],"class_list":["post-40496","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-gan","tag-pytorch","tag-50","tag-207","tag-86","tag-1821","tag-427","tag-3548"],"yoast_head":"<!-- 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