{"id":2707,"date":"2025-01-25T12:12:23","date_gmt":"2025-01-25T04:12:23","guid":{"rendered":"https:\/\/www.gnn.club\/?p=2707"},"modified":"2025-03-12T15:06:36","modified_gmt":"2025-03-12T07:06:36","slug":"tutorial-07-%e9%92%88%e9%94%8b%e7%9b%b8%e5%af%b9%ef%bc%9a%e5%af%b9%e6%8a%97%e5%ad%a6%e4%b9%a0%ef%bc%88adversarial-learning%ef%bc%89","status":"publish","type":"post","link":"http:\/\/gnn.club\/?p=2707","title":{"rendered":"Tutorial 07 \u2013 \u9488\u950b\u76f8\u5bf9\uff1a\u5bf9\u6297\u5b66\u4e60\uff08Adversarial Learning\uff09"},"content":{"rendered":"

Learning Methods of Deep Learning<\/h1>\n
\n

create by Deepfinder<\/p>\n

Agenda<\/h3>\n
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    \n
  1. \u5e08\u5f92\u76f8\u6388\uff1a\u6709\u76d1\u7763\u5b66\u4e60\uff08Supervised Learning\uff09<\/li>\n
  2. \u89c1\u5fae\u77e5\u8457\uff1a\u65e0\u76d1\u7763\u5b66\u4e60\uff08Un-supervised Learning\uff09<\/li>\n
  3. \u65e0\u5e08\u81ea\u901a\uff1a\u81ea\u76d1\u7763\u5b66\u4e60\uff08Self-supervised Learning\uff09<\/li>\n
  4. \u4ee5\u70b9\u5e26\u9762\uff1a\u534a\u76d1\u7763\u5b66\u4e60\uff08Semi-supervised learning\uff09<\/li>\n
  5. \u660e\u8fa8\u662f\u975e\uff1a\u5bf9\u6bd4\u5b66\u4e60\uff08Contrastive Learning\uff09<\/li>\n
  6. \u4e3e\u4e00\u53cd\u4e09\uff1a\u8fc1\u79fb\u5b66\u4e60\uff08Transfer Learning\uff09<\/li>\n
  7. \u9488\u950b\u76f8\u5bf9\uff1a\u5bf9\u6297\u5b66\u4e60\uff08Adversarial Learning\uff09<\/strong><\/li>\n
  8. \u4f17\u5fd7\u6210\u57ce\uff1a\u96c6\u6210\u5b66\u4e60(Ensemble Learning) <\/li>\n
  9. \u6b8a\u9014\u540c\u5f52\uff1a\u8054\u90a6\u5b66\u4e60\uff08Federated Learning\uff09<\/li>\n
  10. \u767e\u6298\u4e0d\u6320\uff1a\u5f3a\u5316\u5b66\u4e60\uff08Reinforcement Learning\uff09<\/li>\n
  11. \u6c42\u77e5\u82e5\u6e34\uff1a\u4e3b\u52a8\u5b66\u4e60\uff08Active Learning\uff09<\/li>\n
  12. \u4e07\u6cd5\u5f52\u5b97\uff1a\u5143\u5b66\u4e60\uff08Meta-Learning\uff09<\/li>\n<\/ol>\n

    The Adversarial Mechanism<\/h2>\n
    \n

    \u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08GAN\uff09\u662f\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u7684\u4e00\u4e2a\u9769\u547d\u6027\u6982\u5ff5\uff0c\u4e3a\u6570\u636e\u751f\u6210\u63d0\u4f9b\u4e86\u4e00\u79cd\u5168\u65b0\u7684\u65b9\u5f0f\u3002\u5176\u540d\u79f0\u4e2d\u7684\u5bf9\u6297<\/strong>\u4f53\u73b0\u4e86\u6838\u5fc3\u601d\u60f3\uff1a\u901a\u8fc7\u4e24\u4e2a\u795e\u7ecf\u7f51\u7edc\u4e4b\u95f4\u7684\u76f8\u4e92\u7ade\u4e89\u6765\u751f\u6210\u6570\u636e\u3002<\/p>\n

    \u8fd9\u4e24\u4e2a\u7f51\u7edc\u5206\u522b\u662f\uff1a\u751f\u6210\u5668 (Generator) \u548c\u5224\u522b\u5668 (Discriminator)\u3002<\/p>\n

    \u60f3\u8c61\u4e00\u4e2a\u4f8b\u5b50\uff0c\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u5982\u540c\u4e00\u573a\u7cbe\u5fc3\u7f16\u6392\u7684\u827a\u672f\u8868\u6f14\u3002\u821e\u53f0\u4e0a\u6709\u4e24\u4f4d\u4e3b\u8981\u7684\u827a\u672f\u5bb6\uff1a\u751f\u6210\u5668\u548c\u5224\u522b\u5668\u3002<\/p>\n

      \n
    • \n

      \u751f\u6210\u5668\u5145\u6ee1\u521b\u610f\u548c\u9b54\u6cd5\uff0c\u4ece\u65e0\u4e2d\u521b\u9020\uff0c\u6325\u52a8\u753b\u7b14\uff0c\u5c1d\u8bd5\u5236\u4f5c\u6700\u7f8e\u7684\u753b\u4f5c\u3002\u5b83\u4ece\u4e00\u4e2a\u968f\u673a\u7684\u7075\u611f\uff08\u566a\u58f0\u5411\u91cf\uff09\u51fa\u53d1\uff0c\u8bd5\u56fe\u521b\u4f5c\u4ee4\u4eba\u4fe1\u670d\u7684\u4f5c\u54c1\u3002<\/p>\n<\/li>\n

    • \n

      \u800c\u5728\u821e\u53f0\u7684\u53e6\u4e00\u4fa7\uff0c\u5224\u522b\u5668\u5219\u626e\u6f14\u7740\u6279\u8bc4\u5bb6\u7684\u89d2\u8272\uff0c\u76ee\u5149\u9510\u5229\uff0c\u4e0d\u653e\u8fc7\u4efb\u4f55\u7455\u75b5\u3002\u5f53\u5b83\u9762\u524d\u5c55\u793a\u7684\u4f5c\u54c1\u6765\u6e90\u4e8e\u771f\u5b9e\u4e16\u754c\u65f6\uff0c\u5b83\u6b23\u7136\u70b9\u5934\uff1b\u4f46\u5f53\u4f5c\u54c1\u51fa\u81ea\u751f\u6210\u5668\u4e4b\u624b\uff0c\u5b83\u4fbf\u7ec6\u7ec6\u5ba1\u67e5\uff0c\u51b3\u5b9a\u8fd9\u662f\u771f\u54c1\u8fd8\u662f\u8d5d\u54c1\u3002\u8fd9\u4e2a\u5224\u522b\u8fc7\u7a0b\u4e0d\u65ad\u5730\u53cd\u9988\u7ed9\u751f\u6210\u5668\uff0c\u544a\u8bc9\u5b83\u5728\u54ea\u91cc\u505a\u5f97\u4e0d\u591f\u597d\uff0c\u9700\u8981\u6539\u8fdb\u3002<\/p>\n<\/li>\n

    • \n

      \u8fd9\u573a\u821e\u8e48\u662f\u4e00\u4e2a\u6301\u7eed\u7684\u8fed\u4ee3\u8fc7\u7a0b\uff0c\u53cc\u65b9\u4e92\u76f8\u6311\u6218\uff0c\u5171\u540c\u6210\u957f\u3002<\/p>\n<\/li>\n

    • \n

      \u968f\u7740\u65f6\u95f4\u7684\u6d41\u901d\uff0c\u751f\u6210\u5668\u7684\u6280\u5de7\u53d8\u5f97\u8d8a\u6765\u8d8a\u7eaf\u719f\uff0c\u800c\u5224\u522b\u5668\u7684\u9274\u8d4f\u80fd\u529b\u4e5f\u65e5\u76ca\u63d0\u9ad8\u3002\u6700\u7ec8\uff0c\u6211\u4eec\u5e0c\u671b\u5728\u8fd9\u573a\u821e\u8e48\u4e2d\uff0c\u751f\u6210\u5668\u80fd\u591f\u521b\u4f5c\u51fa\u5982\u6b64\u9ad8\u8d28\u91cf\u7684\u4f5c\u54c1\uff0c\u4ee5\u81f3\u4e8e\u5373\u4f7f\u662f\u6700\u5c16\u9510\u7684\u6279\u8bc4\u5bb6\u2014\u2014\u5224\u522b\u5668\uff0c\u4e5f\u65e0\u6cd5\u533a\u5206\u5176\u771f\u4f2a\u3002<\/p>\n<\/li>\n<\/ul>\n

      \u5177\u4f53\u6765\u8bf4\uff1a<\/p>\n

      \u5728\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u7684\u821e\u53f0\u4e0a\uff0c\u751f\u6210\u5668\u626e\u6f14\u7740\u4e00\u4e2a\u5145\u6ee1\u521b\u610f\u7684\u827a\u672f\u5bb6\u89d2\u8272\u3002\u8fd9\u4f4d\u201c\u827a\u672f\u5bb6\u201d\u4ece\u4e00\u4e2a\u968f\u673a\u5411\u91cf\u4e2d\u6c72\u53d6\u7075\u611f\uff0c\u901a\u8fc7\u4e00\u7cfb\u5217\u795e\u7ecf\u7f51\u7edc\u5c42\uff08\u5982\u5377\u79ef\u6216\u5168\u8fde\u63a5\u5c42\uff09\u5c06\u5176\u8f6c\u5316\u4e3a\u6709\u5f62\u7684\u4f5c\u54c1\u3002\u4e0e\u771f\u5b9e\u4e16\u754c\u7684\u827a\u672f\u5bb6\uff08\u771f\u5b9e\u7684\u6570\u636e\uff09\u4e0d\u65ad\u7ec3\u4e60\u548c\u4fee\u6b63\u6280\u5de7\u4ee5\u5b8c\u5584\u4f5c\u54c1\u7684\u8fc7\u7a0b\u76f8\u4f3c\uff0c\u751f\u6210\u5668\u4e5f\u4e0d\u65ad\u5730\u8c03\u6574\u81ea\u5df1\u7684\u53c2\u6570\uff0c\u4ee5\u4f7f\u5176\u4ea7\u751f\u7684\u4f5c\u54c1\u66f4\u52a0\u903c\u771f\u3002\u5176\u76ee\u6807\u662f\u521b\u4f5c\u51fa\u4ee4\u4eba\u4fe1\u670d\u7684\u6570\u636e\uff0c\u4ee5\u81f3\u4e8e\u5224\u522b\u5668\u2014\u2014\u8fd9\u4f4d\u4e25\u683c\u7684\u827a\u672f\u8bc4\u8bba\u5bb6\uff0c\u96be\u4ee5\u533a\u5206\u5176\u771f\u4f2a\u3002\u56e0\u6b64\uff0c\u751f\u6210\u5668\u4e0d\u4ec5\u662f\u4e00\u4e2a\u521b\u4f5c\u8005\uff0c\u66f4\u662f\u4e00\u4e2a\u7ec8\u8eab\u5b66\u4e60\u8005\uff0c\u4e0d\u65ad\u5730\u901a\u8fc7\u5224\u522b\u5668\u7684\u53cd\u9988\u6765\u5b8c\u5584\u81ea\u5df1\u7684\u201c\u827a\u672f\u6280\u5de7\u201d<\/strong>\u3002<\/p>\n

      \u800c\u5224\u522b\u5668\u662f\u90a3\u4f4d\u6279\u5224\u773c\u5149\u7280\u5229\u7684\u827a\u672f\u8bc4\u8bba\u5bb6\u3002\u5b83\u5bf9\u6bcf\u4e00\u4ef6\u4f5c\u54c1\u90fd\u8fdb\u884c\u4e25\u683c\u7684\u5ba1\u67e5\uff0c\u901a\u8fc7\u5176\u5185\u90e8\u7531\u591a\u4e2a\u795e\u7ecf\u7f51\u7edc\u5c42\uff08\u4f8b\u5982\u5377\u79ef\u5c42\u6216\u5168\u8fde\u63a5\u5c42\uff09\u7ec4\u6210\u7684\u590d\u6742\u673a\u5236\uff0c\u5224\u5b9a\u8fd9\u4ef6\u4f5c\u54c1\u662f\u5426\u4e3a\u771f\u5b9e\u4e16\u754c\u7684\u4f73\u4f5c\uff0c\u8fd8\u662f\u751f\u6210\u5668\u6240\u521b\u4f5c\u7684\u6a21\u4eff\u54c1\u3002\u5224\u522b\u5668\u5728\u63a5\u6536\u5230\u6570\u636e\u540e\uff0c\u901a\u8fc7\u5176\u7f51\u7edc\u7ed3\u6784\u8f93\u51fa\u4e00\u4e2a\u8bc4\u5206\uff0c\u8868\u793a\u8fd9\u4efd\u6570\u636e\u7684\u771f\u5b9e\u6027\u6982\u7387\u3002\u5176\u6838\u5fc3\u4efb\u52a1\u662f\u6b63\u786e\u5730\u8bc6\u522b\u51fa\u771f\u5b9e\u6570\u636e\u548c\u751f\u6210\u6570\u636e\uff0c\u5e76\u901a\u8fc7\u5176\u5224\u65ad\u4e3a\u751f\u6210\u5668\u63d0\u4f9b\u5b9d\u8d35\u7684\u53cd\u9988\uff0c\u4f7f\u5176\u6709\u673a\u4f1a\u66f4\u8fdb\u4e00\u6b65\u5730\u5b8c\u5584\u81ea\u5df1\u7684\u521b\u4f5c\u6280\u80fd\u3002\u56e0\u6b64\uff0c\u5224\u522b\u5668\u65e2\u662f\u4e00\u4e2a\u4e25\u82db\u7684\u8bc4\u5ba1\uff0c\u4e5f\u662f\u751f\u6210\u5668\u6210\u957f\u9053\u8def\u4e0a\u7684\u5173\u952e\u5f15\u5bfc\u8005<\/strong>\u3002<\/p>\n

      \n \n<\/p>\n

      \nimport torch as t\nfrom torch import nn\nfrom torch.autograd import Variable\nfrom torch.optim import Adam\nfrom torchvision import transforms\nfrom torchvision.utils import make_grid\nfrom torchvision.datasets import CIFAR10, MNIST\nfrom pylab import plt\n%matplotlib inline\n\nclass Config:\n    lr = 0.0002\n    nz = 100 # noise dimension\n    image_size = 64\n    image_size2 = 64\n    nc = 1 # chanel of img \n    ngf = 64 # generate channel\n    ndf = 64 # discriminative channel\n    beta1 = 0.5\n    batch_size = 32\n    max_epoch = 10 # =1 when debug\n    workers = 2\n    gpu = True # use gpu or not\n\nopt=Config()\n\n# data preprocess\ntransform=transforms.Compose([\n                transforms.Resize(opt.image_size),\n                transforms.ToTensor(),\n                transforms.Normalize([0.5], [0.5])\n                ])\n\ndataset=MNIST(root='data', transform=transform, download=True)\n# dataloader with multiprocessing\ndataloader=t.utils.data.DataLoader(dataset,\n                                   opt.batch_size,\n                                   shuffle=True,\n                                   num_workers=opt.workers)\n# define model\nnetg = nn.Sequential(\n    nn.ConvTranspose2d(opt.nz,opt.ngf*8,4,1,0,bias=False),\n    nn.BatchNorm2d(opt.ngf*8),\n    nn.ReLU(True),\n\n    nn.ConvTranspose2d(opt.ngf*8,opt.ngf*4,4,2,1,bias=False),\n    nn.BatchNorm2d(opt.ngf*4),\n    nn.ReLU(True),\n\n    nn.ConvTranspose2d(opt.ngf*4,opt.ngf*2,4,2,1,bias=False),\n    nn.BatchNorm2d(opt.ngf*2),\n    nn.ReLU(True),\n\n    nn.ConvTranspose2d(opt.ngf*2,opt.ngf,4,2,1,bias=False),\n    nn.BatchNorm2d(opt.ngf),\n    nn.ReLU(True),\n\n    nn.ConvTranspose2d(opt.ngf,opt.nc,4,2,1,bias=False),\n    nn.Tanh()\n)\n\nnetd = nn.Sequential(\n    nn.Conv2d(opt.nc,opt.ndf,4,2,1,bias=False),\n    nn.LeakyReLU(0.2,inplace=True),\n\n    nn.Conv2d(opt.ndf,opt.ndf*2,4,2,1,bias=False),\n    nn.BatchNorm2d(opt.ndf*2),\n    nn.LeakyReLU(0.2,inplace=True),\n\n    nn.Conv2d(opt.ndf*2,opt.ndf*4,4,2,1,bias=False),\n    nn.BatchNorm2d(opt.ndf*4),\n    nn.LeakyReLU(0.2,inplace=True),\n\n    nn.Conv2d(opt.ndf*4,opt.ndf*8,4,2,1,bias=False),\n    nn.BatchNorm2d(opt.ndf*8),\n    nn.LeakyReLU(0.2,inplace=True),\n\n    nn.Conv2d(opt.ndf*8,1,4,1,0,bias=False),\n    nn.Sigmoid()\n)<\/code><\/pre>\n
      GAN\u6a21\u578b\u4e0e\u635f\u5931\u8be6\u89e3<\/h2>\n
      \n
      \n  \n<\/p>\n
      \u5f53\u8c08\u8bba\u4f20\u7edf\u7684 GAN \u65f6, \u5b83\u7684\u76ee\u6807\u51fd\u6570\u662f\u4e00\u4e2a\u4e24\u4eba\u96f6\u548c\u535a\u5f08, \u5176\u4e2d\u751f\u6210\u5668 ( $G$ ) \u548c\u5224\u522b\u5668 ( $D$ ) \u6709\u5bf9\u7acb\u7684\u76ee\u6807\u3002\u535a\u5f08\u8fc7\u7a0b\u53ef\u4ee5\u8868\u793a\u4e3a:
      \n$$
      \n\\min _G \\max _D \\mathcal{L}(D, G)=\\mathrm{E}_{x \\sim p_{\\text {data }}(x)}[\\log D(x)]+\\mathrm{E}_{z \\sim p_z(z)}[\\log (1-D(G(z)))]
      \n$$<\/p>\n
        \n
      • \u5916\u90e8\u7684\u6700\u5c0f\u5316 (min) \u4ee3\u8868\u751f\u6210\u5668 $G$ \u7684\u76ee\u6807\u3002\u751f\u6210\u5668\u5e0c\u671b\u6700\u5c0f\u5316\u5224\u522b\u5668\u5bf9\u5176\u751f\u6210\u7684\u6837\u672c\u4ea7\u751f\u7684\u6b63\u786e\u5206\u7c7b\u6982\u7387\u3002\u6362\u53e5\u8bdd\u8bf4, \u751f\u6210\u5668\u8bd5\u56fe\u9a97\u8fc7\u5224\u522b\u5668, \u8ba9\u5176\u8ba4\u4e3a\u751f\u6210\u7684\u6837\u672c\u662f\u771f\u5b9e\u7684\u3002<\/li>\n
      • \u5185\u90e8\u7684\u6700\u5927\u5316 (max)\u4ee3\u8868\u5224\u522b\u5668 $D$ \u7684\u76ee\u6807\u3002\u5224\u522b\u5668\u5e0c\u671b\u6700\u5927\u5316\u5176\u5bf9\u771f\u5b9e\u548c\u751f\u6210\u6837\u672c\u7684\u5206\u7c7b\u80fd\u529b\u3002<\/li>\n<\/ul>\n
        \u751f\u6210\u5bf9\u6297\u7f51\u7edc\u7684\u6838\u5fc3\u601d\u60f3\u662f\u5728\u751f\u6210\u5668\uff08Generator\uff09\u548c\u5224\u522b\u5668\uff08Discriminator\uff09\u4e4b\u95f4\u5efa\u7acb\u4e00\u4e2a\u7ade\u4e89\u5173\u7cfb\u3002\u4e3a\u4e86\u4f7f\u8fd9\u79cd\u7ade\u4e89\u6709\u6548\uff0c\u9700\u8981\u4e3a\u8fd9\u4e24\u4e2a\u7f51\u7edc\u5b9a\u4e49\u9002\u5f53\u7684\u635f\u5931\u51fd\u6570\u3002\u5728\u6700\u57fa\u672c\u7684GAN\u4e2d\uff0c\u751f\u6210\u5668\u7684\u4efb\u52a1\u662f\u751f\u6210\u80fd\u591f\u6b3a\u9a97\u5224\u522b\u5668\u7684\u6570\u636e\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u751f\u6210\u5668\u5e0c\u671b\u5224\u522b\u5668\u8ba4\u4e3a\u5176\u751f\u6210\u7684\u6570\u636e\u5c3d\u53ef\u80fd\u5730\u63a5\u8fd1\u771f\u5b9e\u6570\u636e\u3002\u56e0\u6b64\uff0c\u751f\u6210\u5668\u7684\u635f\u5931\u51fd\u6570\u901a\u5e38\u57fa\u4e8e\u5224\u522b\u5668\u5bf9\u751f\u6210\u6570\u636e\u7684\u8bc4\u4f30\u3002<\/p>\n
        \u5047\u8bbe $G$ \u662f\u751f\u6210\u5668, $D$ \u662f\u5224\u522b\u5668\u3002\u5f53\u7ed9\u5b9a\u4e00\u4e2a\u968f\u673a\u566a\u58f0\u5411\u91cf $z$ \u65f6, \u751f\u6210\u5668 $G$ \u751f\u6210\u4e00\u4e2a\u6570\u636e $G(z)$ \u3002<\/p>\n
        \u5224\u522b\u5668 $D$ \u8bc4\u4f30\u8fd9\u4e2a\u6570\u636e\u5e76\u7ed9\u51fa\u4e00\u4e2a\u6982\u7387 $D(G(z))$, \u8868\u793a\u5b83\u8ba4\u4e3a $G(z)$ \u662f\u771f\u5b9e\u6570\u636e\u7684\u6982\u7387\u3002<\/p>\n
        \u751f\u6210\u5668\u5e0c\u671b $D(G(z))$ \u5c3d\u53ef\u80fd\u5730\u63a5\u8fd1 1, \u5373\u5224\u522b\u5668\u88ab\u6b3a\u9a97\u5e76\u8ba4\u4e3a\u751f\u6210\u6570\u636e\u662f\u771f\u5b9e\u7684\u3002<\/p>\n
        \u5355\u72ec\u8003\u8651\u751f\u6210\u5668\uff1a<\/strong><\/p>\n
        \u5982\u679c\u53ea\u8003\u8651\u4ece\u751f\u6210\u5668\u4ea7\u751f\u7684\u56fe\u7247, \u800c\u5ffd\u7565\u771f\u5b9e\u6570\u636e\u7684\u5f71\u54cd $\\left(\\mathrm{E}_{x \\sim p_{d: 18}(x)}[\\log D(x)]=0\\right)$, \u539f\u59cb\u7684GAN\u635f\u5931\u53ef\u4ee5\u7b80\u5199\u4e3a:
        \n$$
        \nL_G=\\mathrm{E}_{z \\sim p_z(z)}[\\log (1-D(G(z)))]
        \n$$<\/p>\n
        \u516c\u5f0f\u89e3\u91ca: <\/p>\n
          \n
        • \u5f53 $D(G(z))$ \u63a5\u8fd1 1 \u65f6, \u610f\u5473\u7740\u5224\u522b\u5668\u51e0\u4e4e\u5b8c\u5168\u786e\u4fe1\u751f\u6210\u7684\u6570\u636e\u662f\u771f\u5b9e\u7684\u3002\u6b64\u65f6, $1-D(G(z))$ \u63a5\u8fd1 0 , \u800c $\\log (1-D(G(z)))$ \u7684\u503c\u4f1a\u662f\u4e00\u4e2a\u5f88\u5927\u7684\u8d1f\u6570\u3002\u8fd9\u6b63\u662f\u6211\u4eec\u6240\u671f\u671b\u7684\u6700\u5c0f\u5316\u751f\u6210\u5668\u635f\u5931\u3002<\/li>\n
        • \u5f53 $D(G(z))$ \u63a5\u8fd1 0 \u65f6, \u610f\u5473\u7740\u5224\u522b\u5668\u8ba4\u4e3a\u751f\u6210\u7684\u6570\u636e\u662f\u5047\u7684\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b, $1-D(G(z))$ \u63a5\u8fd1 1, \u56e0\u6b64 $\\log (1-D(G(z)))$ \u63a5\u8fd1 0 \u3002\u751f\u6210\u5668\u4f1a\u5c3d\u91cf\u907f\u514d\u8fd9\u79cd\u60c5\u51b5, \u56e0\u4e3a\u751f\u6210\u5668\u7684\u76ee\u6807\u662f\u6700\u5c0f\u5316 $\\log (1-D(G(z)))$, \u8fd9\u5b9e\u9645\u4e0a\u662f\u9f13\u52b1\u751f\u6210\u5668\u4ea7\u751f\u80fd\u591f\u6b3a\u9a97\u5224\u522b\u5668\u7684\u6570\u636e\u3002<\/li>\n<\/ul>\n
           \u4e3a\u4ec0\u4e48\u635f\u5931\u516c\u5f0f\u4e2d\u4f1a\u5b58\u5728\u4e00\u4e2alog\uff1f<\/strong><\/h4>\n
          \u5355\u72ec\u8003\u8651\u5224\u522b\u5668\uff1a<\/strong><\/p>\n
          \u5982\u679c\u53ea\u8003\u8651\u5224\u522b\u5668\u7684\u89d2\u5ea6\uff0cGAN\u7684\u635f\u5931\u51fd\u6570\u4e3b\u8981\u5173\u6ce8\u4e8e\u5224\u522b\u5668\u5982\u4f55\u533a\u5206\u771f\u5b9e\u6570\u636e\u548c\u751f\u6210\u7684\u6570\u636e\u3002\u5bf9\u4e8e\u5224\u522b\u5668 $D$, \u635f\u5931\u51fd\u6570\u4e3a:
          \n$$
          \nL_D=\\mathrm{E}_{x \\sim p_{\\text {data }}(x)}[\\log D(x)]+\\mathrm{E}_{z \\sim p_z(z)}[\\log (1-D(G(z)))]
          \n$$<\/p>\n
          \u635f\u5931\u51fd\u6570\u7531\u4e24\u90e8\u5206\u7ec4\u6210:<\/p>\n
          (1) $\\mathrm{E}_{x \\sim p_{\\text {data}}(x)}[\\log D(x)]$ : \u8fd9\u90e8\u5206\u662f\u5173\u4e8e\u771f\u5b9e\u6570\u636e\u7684\u3002\u5224\u522b\u5668 $D$ \u8bd5\u56fe\u6700\u5927\u5316\u5bf9\u771f\u5b9e\u6570\u636e\u6837\u672c $x$ \u7684\u6b63\u786e\u5206\u7c7b\u6982\u7387\u3002\u6362\u53e5\u8bdd\u8bf4, \u5b83\u5e0c\u671b\u5bf9\u4e8e\u6765\u81ea\u771f\u5b9e\u6570\u636e\u5206\u5e03\u7684\u6837\u672c $x$, \u8f93\u51fa\u5c3d\u53ef\u80fd\u63a5\u8fd1 1 \u3002<\/p>\n
          (2) $\\mathrm{E}_{z \\sim p_(z)}[\\log (1-D(G(z)))]$ : \u8fd9\u90e8\u5206\u662f\u5173\u4e8e\u751f\u6210\u7684\u6570\u636e\u7684\u3002\u5224\u522b\u5668 $D$ \u8bd5\u56fe\u6700\u5927\u5316\u5176\u5bf9\u751f\u6210\u6570\u636e\u7684\u6b63\u786e\u5206\u7c7b\u6982\u7387, \u5373\u5c06\u5176\u5206\u7c7b\u4e3a\u5047\u7684\u3002\u8fd9\u610f\u5473\u7740, \u5bf9\u4e8e\u4ece\u5148\u9a8c\u566a\u58f0\u5206\u5e03 $p_z$ \u4e2d\u91c7\u6837\u7136\u540e\u901a\u8fc7\u751f\u6210\u5668 $G$ \u751f\u6210\u7684\u5047\u6837\u672c, \u5224\u522b\u5668\u7684\u8f93\u51fa\u5e94\u8be5\u5c3d\u53ef\u80fd\u63a5\u8fd1 0 \u3002<\/p>\n
          \u5224\u522b\u5668 $D$ \u7684\u76ee\u6807\u662f\u6700\u5927\u5316\u635f\u5931\u51fd\u6570<\/strong>\u3002\u8fd9\u610f\u5473\u7740, \u4e3a\u4e86\u8fbe\u5230\u6700\u4f73\u6548\u679c, \u5224\u522b\u5668\u5e0c\u671b\u80fd\u591f\u51c6\u786e\u5730\u533a\u5206\u771f\u5b9e\u6570\u636e\u548c\u751f\u6210\u7684\u6570\u636e\u3002\u5728\u6700\u7406\u60f3\u7684\u60c5\u51b5\u4e0b, \u5bf9\u4e8e\u771f\u5b9e\u6570\u636e, $D(x)=1$; \u800c\u5bf9\u4e8e\u751f\u6210\u7684\u6570\u636e\uff0c $D(G(x))=0$\u3002<\/p>\n
          \u4f46\u5728\u5b9e\u9645\u8bad\u7ec3\u4e2d\uff0c\u8fd9\u79cd\u7406\u60f3\u60c5\u51b5\u5f88\u5c11\u8fbe\u5230\uff0c\u56e0\u4e3a\u751f\u6210\u5668\u4e5f\u5728\u5c1d\u8bd5\u6539\u8fdb\u81ea\u5df1\uff0c\u751f\u6210\u66f4\u903c\u771f\u7684\u6837\u672c\u6765\u6b3a\u9a97\u5224\u522b\u5668\u3002<\/p>\n
          # optimizer\noptimizerD = Adam(netd.parameters(),lr=opt.lr,betas=(opt.beta1,0.999))\noptimizerG = Adam(netg.parameters(),lr=opt.lr,betas=(opt.beta1,0.999))\n\n# criterion\ncriterion = nn.BCELoss()\n\nfix_noise = Variable(t.FloatTensor(opt.batch_size,opt.nz,1,1).normal_(0,1))\nif opt.gpu:\n    fix_noise = fix_noise.cuda()\n    netd.cuda()\n    netg.cuda()\n    criterion.cuda()  <\/code><\/pre>\n
          GAN\u6a21\u578b\u7684\u8bad\u7ec3<\/h3>\n
          \n
          GAN \u6a21\u578b\u5728\u5f00\u59cb\u8bad\u7ec3\u4e4b\u524d, \u9996\u5148\u9700\u8981\u9009\u62e9\u4e00\u4e2a\u5408\u9002\u7684\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3002\u4f8b\u5982, \u5bf9\u4e8e\u56fe\u50cf\u751f\u6210, \u4e00\u822c\u57fa\u4e8e\u5377\u79ef\u7684\u7ed3\u6784\u504f\u591a\u3002\u521d\u59cb\u5316\u751f\u6210\u5668 $G$ \u548c\u5224\u522b\u5668 $D$ \u7684\u6743\u91cd, \u901a\u5e38\u4f7f\u7528\u5c0f\u7684\u968f\u673a\u503c\u3002 GAN \u5305\u62ec\u4e24\u4e2a\u7f51\u7edc: \u751f\u6210\u5668\u548c\u5224\u522b\u5668, \u5b83\u4eec\u9700\u8981\u4ea4\u66ff\u6216\u540c\u65f6\u8bad\u7ec3\u3002GAN \u7684\u5faa\u73af\u8bad\u7ec3\u5927\u81f4\u5982\u4e0b:<\/p>\n
            \n
          • \u9996\u5148\u8bad\u7ec3\u5224\u522b\u5668\uff0c\u4f7f\u7528\u5f53\u524d\u7684\u751f\u6210\u5668\u751f\u6210\u5047\u6570\u636e\u548c\u771f\u5b9e\u6570\u636e\u8bad\u7ec3\u5224\u522b\u5668\uff0c\u5224\u522b\u5668\u7684\u76ee\u6807\u662f\u6b63\u786e\u5730\u533a\u5206\u771f\u5b9e\u6570\u636e\u548c\u5047\u6570\u636e\u3002<\/li>\n<\/ul>\n
            \u5177\u4f53\u6765\u8bf4\uff0c\u4e00\u65b9\u9762\u4ece\u771f\u5b9e\u6570\u636e\u5206\u5e03\u4e2d\u62bd\u53d6\u4e00\u4e2a\u6279\u91cf\u7684\u6570\u636e $x$, \u8ba1\u7b97\u5224\u522b\u5668 $D$ \u5728\u771f\u5b9e\u6570\u636e\u4e0a\u7684\u8f93\u51fa $D(x)$, \u8ba1\u7b97\u635f\u5931 $\\mathrm{E}_{x \\sim p_{\\text {deta }}(x)}[\\log D(x)]$ \u3002\u53e6\u4e00\u65b9\u9762\u4ece\u968f\u673a\u566a\u58f0\u5206\u5e03\u4e2d\u62bd\u53d6\u4e00\u4e2a\u6279\u91cf\u7684\u566a\u58f0 $z$ \u3002\u4f7f\u7528\u751f\u6210\u5668 $G$ \u751f\u6210\u4e00\u4e2a\u6279\u91cf\u7684\u5047\u6570\u636e $G(z)$ \u3002\u8ba1\u7b97\u5224\u522b\u5668 $D$ \u5728\u5047\u6570\u636e\u4e0a\u7684\u8f93\u51fa $D(G(z))$, \u8ba1\u7b97\u635f\u5931 $\\mathrm{E}_{z \\sim p_z(z)}[\\log (1-D(G(z)))]$ \u3002\u5408\u5e76\u771f\u5b9e\u6570\u636e\u548c\u751f\u6210\u6570\u636e\u7684\u635f\u5931\uff0c\u4f7f\u7528\u8fd9\u4e2a\u603b\u635f\u5931\u6765\u66f4\u65b0\u5224\u522b\u5668 $D$ \u7684\u6743\u91cd\uff0c\u901a\u5e38\u4f7f\u7528\u4f18\u5316\u5668\u5982Adam\u6216RMSProp\u3002<\/p>\n
              \n
            • \u7136\u540e\u8bad\u7ec3\u751f\u6210\u5668\uff0c\u8bd5\u56fe\u6b3a\u9a97\u5224\u522b\u5668\uff0c\u4f7f\u5176\u8ba4\u4e3a\u751f\u6210\u7684\u6570\u636e\u662f\u771f\u5b9e\u7684\uff0c\u751f\u6210\u5668\u7684\u76ee\u6807\u662f\u751f\u6210\u80fd\u591f\u88ab\u5224\u522b\u5668\u8bef\u5224\u4e3a\u771f\u5b9e\u6570\u636e\u7684\u6570\u636e\u3002<\/li>\n<\/ul>\n
              \u5177\u4f53\u6765\u8bf4\uff0c\u4ece\u968f\u673a\u566a\u58f0\u5206\u5e03\u4e2d\u518d\u6b21\u62bd\u53d6\u4e00\u4e2a\u6279\u91cf\u7684\u566a\u58f0 $Z$, \u901a\u8fc7\u5224\u522b\u5668 $D$ \u8bc4\u4f30\u751f\u6210\u5668 $G$ \u4ea7\u751f\u7684\u5047\u6570\u636e, \u8ba1\u7b97\u635f\u5931 $\\mathrm{E}_{z \\sim p_z(z)}[\\log (1-D(G(z)))]$,\u4f7f\u7528\u8be5\u635f\u5931\u66f4\u65b0\u751f\u6210\u5668 $G$ \u7684\u6743\u91cd\u3002<\/p>\n
              \u6bcf\u9694\u51e0\u4e2a\u8f6e\u6b21\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e9b\u6307\u6807\u6765\u8bc4\u4f30\u751f\u6210\u5668\u7684\u8f93\u51fa\u3002\u91cd\u590d\u4e0a\u8ff0\u8bad\u7ec3\u6b65\u9aa4\u76f4\u5230\u6ee1\u8db3\u7ec8\u6b62\u6761\u4ef6\uff0c\u8fd9\u53ef\u4ee5\u662f\u9884\u5b9a\u7684\u8bad\u7ec3\u8f6e\u6570\u3001\u6a21\u578b\u6027\u80fd\u8fbe\u5230\u67d0\u4e2a\u9608\u503c\u6216\u5176\u5b83\u6761\u4ef6\u3002\u5982\u679c\u672a\u6ee1\u8db3\u6761\u4ef6\uff0c\u8fd4\u56de\u5e76\u5f00\u59cb\u65b0\u7684\u8bad\u7ec3\u5faa\u73af\u3002<\/p>\n
              \u5728\u5faa\u73af\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u751f\u6210\u5668\u548c\u5224\u522b\u5668\u90fd\u4f1a\u9010\u6e10\u6539\u8fdb\uff0c\u4e89\u53d6\u66f4\u597d\u5730\u6267\u884c\u5176\u4efb\u52a1\u3002\u6700\u7ec8\u7684\u76ee\u6807\u662f\u627e\u5230\u4e00\u4e2a\u5e73\u8861\u70b9\uff0c\u751f\u6210\u5668\u751f\u6210\u7684\u6570\u636e\u4e0e\u771f\u5b9e\u6570\u636e\u51e0\u4e4e\u65e0\u6cd5\u533a\u5206\u3002\u8fd9\u79cd\u9010\u6b65\u7684\u3001\u53cd\u590d\u7684\u8bad\u7ec3\u65b9\u6cd5\u5141\u8bb8\u6a21\u578b\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u548c\u9002\u5e94\uff0c\u8fd9\u662f\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6210\u529f\u7684\u5173\u952e\u3002<\/strong><\/p>\n
              \n  \n<\/p>\n
              import matplotlib.pyplot as plt\n\n# \u5b58\u50a8\u6bcf\u4e2a\u8fed\u4ee3\u7684\u635f\u5931\nlosses_D = []\nlosses_G = []\n\nfor epoch in range(opt.max_epoch):\n    for ii, data in enumerate(dataloader, 0):\n        real, _ = data\n        input = Variable(real)  # \u5c06\u771f\u5b9e\u56fe\u50cf\u5305\u88c5\u4e3aPyTorch\u53d8\u91cf\uff0c\u7528\u4e8e\u8ba1\u7b97\u56fe\u4e2d\n        label = Variable(t.ones(input.size(0)))  # \u521b\u5efa\u4e0e\u771f\u5b9e\u56fe\u50cf\u6570\u91cf\u76f8\u540c\u7684\u6807\u7b7e\u53d8\u91cf\uff0c\u6240\u6709\u503c\u4e3a1\uff0c\u8868\u793a\u771f\u5b9e\u6570\u636e\n        noise = t.randn(input.size(0), opt.nz, 1, 1)  # \u751f\u6210\u4e0e\u771f\u5b9e\u56fe\u50cf\u6570\u91cf\u76f8\u540c\u7684\u968f\u673a\u566a\u58f0\uff0c\u7528\u4e8e\u751f\u6210\u5047\u56fe\u50cf\n        noise = Variable(noise)  # \u5c06\u968f\u673a\u566a\u58f0\u5305\u88c5\u4e3aPyTorch\u53d8\u91cf\uff0c\u7528\u4e8e\u8ba1\u7b97\u56fe\u4e2d\n\n        if opt.gpu:\n            noise = noise.cuda()\n            input = input.cuda()\n            label = label.cuda()\n\n        # ----- train netd -----\n        netd.zero_grad()\n        ## train netd with real img\n        output = netd(input)\n        error_real = criterion(output.squeeze(), label)\n        error_real.backward()\n        D_x = output.data.mean()\n        ## train netd with fake img\n        fake_pic = netg(noise).detach()\n        output2 = netd(fake_pic)\n        label.data.fill_(0)  # 0 for fake\n        error_fake = criterion(output2.squeeze(), label)\n        error_fake.backward()\n        D_x2 = output2.data.mean()\n        error_D = error_real + error_fake\n        optimizerD.step()\n\n        # ------ train netg -------\n        netg.zero_grad()\n        label.data.fill_(1)\n        noise.data.normal_(0, 1)\n        fake_pic = netg(noise)\n        output = netd(fake_pic)\n        error_G = criterion(output.squeeze(), label)\n        error_G.backward()\n        optimizerG.step()\n        D_G_z2 = output.data.mean()\n\n        # \u5b58\u50a8\u635f\u5931\u503c\n        losses_D.append(error_D.item())\n        losses_G.append(error_G.item())\n\n        if ii % 500 == 0:\n            print(f"Iteration {ii}\/{epoch}: "\n                  f"Discriminator Loss: {error_D.item():.4f}, "\n                  f"Generator Loss: {error_G.item():.4f}, "\n                  f"D(x): {D_x:.4f}, "\n                  f"D(G(z)) (on fake data): {D_G_z2:.4f}, "\n                  f"D(G(z)) (on real data): {D_x2:.4f}")\n    if epoch % 2 == 0:\n        fake_u = netg(fix_noise)\n        imgs = make_grid(fake_u.data * 0.5 + 0.5).cpu()  # CHW\n        plt.imshow(imgs.permute(1, 2, 0).numpy())  # HWC\n        plt.show()\n\n# \u7ed8\u5236\u635f\u5931\u56fe\u50cf\nplt.figure(figsize=(10, 5))\nplt.title("Generator and Discriminator Loss During Training")\nplt.plot(losses_G, label="G")\nplt.plot(losses_D, label="D")\nplt.xlabel("iterations")\nplt.ylabel("Loss")\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
              Iteration 0\/0: Discriminator Loss: 0.0084, Generator Loss: 6.3695, D(x): 0.9990, D(G(z)) (on fake data): 0.0050, D(G(z)) (on real data): 0.0072\nIteration 500\/0: Discriminator Loss: 0.0135, Generator Loss: 5.0962, D(x): 0.9909, D(G(z)) (on fake data): 0.0149, D(G(z)) (on real data): 0.0044\nIteration 1000\/0: Discriminator Loss: 0.0010, Generator Loss: 7.0314, D(x): 0.9996, D(G(z)) (on fake data): 0.0019, D(G(z)) (on real data): 0.0006\nIteration 1500\/0: Discriminator Loss: 0.7012, Generator Loss: 1.0979, D(x): 0.6444, D(G(z)) (on fake data): 0.4389, D(G(z)) (on real data): 0.1342<\/code><\/pre>\n
              \n  \n<\/p>\n
              Iteration 0\/1: Discriminator Loss: 0.0329, Generator Loss: 3.9298, D(x): 0.9901, D(G(z)) (on fake data): 0.0524, D(G(z)) (on real data): 0.0220\nIteration 500\/1: Discriminator Loss: 0.0065, Generator Loss: 6.4350, D(x): 0.9996, D(G(z)) (on fake data): 0.0025, D(G(z)) (on real data): 0.0060\nIteration 1000\/1: Discriminator Loss: 0.2043, Generator Loss: 4.6965, D(x): 0.9954, D(G(z)) (on fake data): 0.0191, D(G(z)) (on real data): 0.1569\nIteration 1500\/1: Discriminator Loss: 0.0110, Generator Loss: 6.9614, D(x): 0.9992, D(G(z)) (on fake data): 0.0025, D(G(z)) (on real data): 0.0101\nIteration 0\/2: Discriminator Loss: 0.0039, Generator Loss: 8.1376, D(x): 0.9999, D(G(z)) (on fake data): 0.0004, D(G(z)) (on real data): 0.0039\nIteration 500\/2: Discriminator Loss: 0.0001, Generator Loss: 9.3291, D(x): 1.0000, D(G(z)) (on fake data): 0.0001, D(G(z)) (on real data): 0.0001\nIteration 1000\/2: Discriminator Loss: 0.0001, Generator Loss: 9.8352, D(x): 1.0000, D(G(z)) (on fake data): 0.0001, D(G(z)) (on real data): 0.0001\nIteration 1500\/2: Discriminator Loss: 0.0001, Generator Loss: 9.6669, D(x): 1.0000, D(G(z)) (on fake data): 0.0001, D(G(z)) (on real data): 0.0001<\/code><\/pre>\n
              Improved GAN<\/h2>\n
              \n
              \u201cImproved Techniques for Training GANs\u201d\u662f\u4e00\u7bc7\u7531Ian J. Goodfellow \u548c\u4ed6\u7684\u540c\u4e8b\u5728 2016 \u5e74\u53d1\u8868\u7684\u8bba\u6587\uff0c\u8fd9\u7bc7\u8bba\u6587\u5bf9\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08GANs\uff09\u7684\u8bad\u7ec3\u8fc7\u7a0b\u505a\u51fa\u4e86\u91cd\u8981\u7684\u6539\u8fdb\u548c\u63d0\u8bae\u3002\u8fd9\u4e9b\u6539\u8fdb\u4e3b\u8981\u96c6\u4e2d\u5728\u63d0\u9ad8GANs\u7684\u7a33\u5b9a\u6027\u548c\u6027\u80fd\u4e0a\uff0c\u89e3\u51b3\u4e86\u4e00\u4e9b\u65e9\u671fGANs\u8bad\u7ec3\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u4f8b\u5982\u6a21\u5f0f\u5d29\u6e83\uff08mode collapse\uff09<\/strong>\u3002<\/p>\n
              \n  \n<\/p>\n
              \u5f53\u8bad\u7ec3GAN\u65f6\uff0c\u7406\u60f3\u60c5\u51b5\u4e0b\u5e0c\u671b\u751f\u6210\u5668\u80fd\u591f\u5b66\u4e60\u5230\u6570\u636e\u5206\u5e03\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u4ee5\u4ea7\u751f\u591a\u6837\u6027\u4e14\u903c\u771f\u7684\u6570\u636e\u3002\u7136\u800c\uff0c\u6a21\u5f0f\u5d29\u6e83\u6307\u7684\u662f\u751f\u6210\u5668\u5f00\u59cb\u751f\u6210\u6781\u5176\u6709\u9650\u7684\u6837\u672c\u79cd\u7c7b\uff0c\u5373\u4fbf\u8fd9\u4e9b\u6837\u672c\u80fd\u591f\u4ee5\u5f88\u9ad8\u7684\u6210\u529f\u7387\u6b3a\u9a97\u5224\u522b\u5668\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u751f\u6210\u5668\u627e\u5230\u4e86\u4e00\u79cd\u201c\u6377\u5f84\u201d\uff0c\u53ea\u751f\u6210\u67d0\u4e9b\u7279\u5b9a\u7684\u6837\u672c\uff08\u8fd9\u4e9b\u6837\u672c\u53ef\u80fd\u5728\u5224\u522b\u5668\u5f53\u524d\u72b6\u6001\u4e0b\u96be\u4ee5\u88ab\u8bc6\u522b\u4e3a\u5047\u7684\uff09\uff0c\u800c\u5ffd\u89c6\u4e86\u6570\u636e\u7684\u5176\u5b83\u7279\u5f81\u548c\u591a\u6837\u6027\u3002\u8fd9\u5bfc\u81f4\u751f\u6210\u7684\u6570\u636e\u867d\u7136\u903c\u771f\uff0c\u4f46\u591a\u6837\u6027\u4e25\u91cd\u4e0d\u8db3\u3002<\/p>\n
              \u53d1\u751f\u6a21\u5f0f\u5d29\u6e83\u7684\u4e3b\u8981\u539f\u56e0\u662fGANs\u6a21\u578b\u7684\u4e0d\u7a33\u5b9a\u6027\u3002\u4f8b\u5982\uff0c\u5982\u679c\u5224\u522b\u5668\u5b66\u4e60\u5f97\u592a\u5feb\uff0c\u751f\u6210\u5668\u53ef\u80fd\u4f1a\u627e\u5230\u5e76\u91cd\u590d\u4f7f\u7528\u80fd\u591f\u901a\u8fc7\u5224\u522b\u5668\u7684\u67d0\u4e9b\u7279\u5b9a\u6a21\u5f0f\uff0c\u800c\u4e0d\u662f\u5b66\u4e60\u66f4\u591a\u6837\u5316\u7684\u6570\u636e\u751f\u6210\u7b56\u7565\u3002\u5373\uff0c\u5982\u679c\u751f\u6210\u5668\u548c\u5224\u522b\u5668\u4e4b\u95f4\u7684\u8bad\u7ec3\u4e0d\u591f\u5e73\u8861\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4e00\u65b9\u8fc7\u4e8e\u5f3a\u5927\uff0c\u4ece\u800c\u4fc3\u4f7f\u53e6\u4e00\u65b9\u91c7\u53d6\u6781\u7aef\u7b56\u7565\u3002<\/strong><\/p>\n
              \u4e0d\u7a33\u5b9a\u6027\u7684\u6839\u6e90\u662f\u7531\u4e8eGANs\u7684\u8bad\u7ec3\u672c\u8d28\u4e0a\u662f\u4e00\u4e2a\u4e24\u4e2a\u7f51\u7edc\uff08\u751f\u6210\u5668\u548c\u5224\u522b\u5668\uff09\u4e4b\u95f4\u7684\u535a\u5f08\u8fc7\u7a0b\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u53ef\u80fd\u4f1a\u975e\u5e38\u4e0d\u7a33\u5b9a\u3002\u5728\u7406\u60f3\u60c5\u51b5\u4e0b\uff0c\u4e24\u8005\u5e94\u8be5\u8fbe\u5230\u7eb3\u4ec0\u5747\u8861\uff0c\u4f46\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\uff0c\u5f80\u5f80\u5f88\u96be\u5b9e\u73b0<\/strong>\u3002\u4f8b\u5982\uff1a<\/p>\n