\n\n \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Network, GAN)\uc740 2014\ub144 Ian Goodfellow\uac00 \uc81c\uc548\ud55c \ud601\uc2e0\uc801\uc778 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc740 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130 \uc0d8\ud50c\uc744 \uc0dd\uc131\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\uba70, \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ube44\ub514\uc624 \uc0dd\uc131, \uc74c\uc131 \ud569\uc131 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\ubc1c\ud788 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 GAN\uc758 \ud6c8\ub828 \uacfc\uc815\uc740 \uc5ec\ub7ec \uac00\uc9c0 \ub09c\uad00\uc5d0 \uc9c1\uba74\ud558\uac8c \ub429\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \uad6c\ud604 \ubc29\ubc95\uacfc \ud568\uaed8 \uc774\ub7ec\ud55c \ub09c\uad00\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uace0, \uc2e4\uc81c \uc608\uc81c \ucf54\ub4dc\uc640 \ud568\uaed8 \ud480\uc774 \uacfc\uc815\uc744 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
1. GAN\uc758 \uae30\ubcf8 \uad6c\uc870<\/h2>\n
\n GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \uad6c\ubd84\uc790(Discriminator)\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c \uc801\ub300\uc801 \uad00\uacc4\uc5d0 \uc788\uc73c\uba70, \uc0dd\uc131\uc790\ub294 \uc9c4\uc9dc\uc640 \uac19\uc740 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uba70, \uad6c\ubd84\uc790\ub294 \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uad6c\ubd84\ud558\ub824\uace0 \ub178\ub825\ud569\ub2c8\ub2e4.\n <\/p>\n
\n \uc774\ub7ec\ud55c \uacfc\uc815\uc740 \uac8c\uc784 \uc774\ub860\uc758 \uac1c\ub150\uacfc \uc720\uc0ac\ud558\uc5ec, \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \ucd5c\uc885\uc801\uc73c\ub85c \uade0\ud615\uc744 \uc774\ub8f0 \ub54c\uae4c\uc9c0 \uacbd\uc7c1\ud569\ub2c8\ub2e4. GAN\uc758 \ubaa9\ud45c\ub294 \uc0dd\uc131\uc790\uac00 \ucda9\ubd84\ud788 \uad6c\ubd84\uc790\ub97c \uc18d\uc77c \uc218 \uc788\uc744 \ub9cc\ud07c \uc9c4\uc9dc \uac19\uc740 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
2. GAN\uc758 \uc218\ud559\uc801 \ubc30\uacbd<\/h2>\n
\n GAN\uc740 \ub450 \uac00\uc9c0 \ud568\uc218\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4: \uc0dd\uc131\uc790 G\uc640 \uad6c\ubd84\uc790 D. \uc0dd\uc131\uc790\ub294 \ubb34\uc791\uc704 \ub178\uc774\uc988 z\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uc9c4\uc9dc\uc640 \uac19\uc740 \ub370\uc774\ud130 x\uc758 \ubd84\ud3ec P_data\ub97c \uadfc\uc0ac\ud558\ub3c4\ub85d \ud559\uc2b5\ud569\ub2c8\ub2e4. \uad6c\ubd84\uc790\ub294 \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uc0dd\uc131\ub41c \uac00\uc9dc \ub370\uc774\ud130\uc758 \ubd84\ud3ec P_g\ub97c \uad6c\ubcc4\ud558\uae30 \uc704\ud574 \ud559\uc2b5\ub429\ub2c8\ub2e4.\n <\/p>\n
\n GAN\uc758 \ubaa9\ud45c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uac8c\uc784\uc801 \ucd5c\uc801 \ubb38\uc81c\ub97c \ud478\ub294 \uac83\uc785\ub2c8\ub2e4:\n <\/p>\n
\n min_G max_D V(D, G) = E[log D(x)] + E[log(1 - D(G(z)))]\n <\/pre>\n\n \uc5ec\uae30\uc11c E\ub294 \uae30\ub300\uac12\uc744 \ub098\ud0c0\ub0b4\uba70, D\ub294 \uc9c4\uc9dc \ub370\uc774\ud130 x\uc5d0 \ub300\ud55c \ud655\ub960\uc744 \uae30\uc900\uc73c\ub85c \ub85c\uadf8\ub97c \ucde8\ud55c \uac83\uc785\ub2c8\ub2e4. GAN\uc758 \ucd5c\uc801\ud654 \ubb38\uc81c\ub294 \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790\uac00 \ub3d9\uc2dc\uc5d0 \ud559\uc2b5\ud558\uc5ec \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac19\uc740 \ubd84\ud3ec\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.\n <\/p>\n
3. GAN \uad6c\ud604\ud558\uae30: \ud30c\uc774\ud1a0\uce58 \uae30\ubcf8 \uc608\uc81c<\/h2>\n
\n \uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\ud604\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 GAN\uc744 \uad6c\ud604\ud574\ubcfc \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
3.1 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc784\ud3ec\ud2b8\ud558\uace0, MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n import torch\n import torch.nn as nn\n import torch.optim as optim\n from torchvision import datasets, transforms\n import matplotlib.pyplot as plt\n import numpy as np\n\n # \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \ub85c\ub4dc\n transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n train_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n <\/pre>\n3.2 \uc0dd\uc131\uc790 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc0dd\uc131\uc790 \ubaa8\ub378\uc740 \uc8fc\uc5b4\uc9c4 \ub178\uc774\uc988 \ubca1\ud130 z\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
\n class Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(100, 256),\n nn.ReLU(),\n nn.Linear(256, 512),\n nn.ReLU(),\n nn.Linear(512, 1024),\n nn.ReLU(),\n nn.Linear(1024, 784),\n nn.Tanh()\n )\n\n def forward(self, z):\n return self.model(z).view(-1, 1, 28, 28)\n\n generator = Generator()\n <\/pre>\n3.3 \uad6c\ubd84\uc790 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uad6c\ubd84\uc790 \ubaa8\ub378\uc740 \uc785\ub825\ubc1b\uc740 \uc774\ubbf8\uc9c0\ub97c \ubc14\ud0d5\uc73c\ub85c \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \uad6c\ubd84\ud569\ub2c8\ub2e4.<\/p>\n
\n class Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(784, 512),\n nn.LeakyReLU(0.2),\n nn.Linear(512, 256),\n nn.LeakyReLU(0.2),\n nn.Linear(256, 1),\n nn.Sigmoid()\n )\n\n def forward(self, img):\n return self.model(img.view(-1, 784))\n\n discriminator = Discriminator()\n <\/pre>\n3.4 \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc124\uc815<\/h3>\n
\uc774\uc81c GAN\uc758 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654\ub97c \uc124\uc815\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n criterion = nn.BCELoss()\n optimizer_G = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n optimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n <\/pre>\n3.5 GAN \ud6c8\ub828 \uacfc\uc815<\/h3>\n
\ub9c8\uc9c0\ub9c9\uc73c\ub85c GAN\uc744 \ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n num_epochs = 200\n for epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(train_loader):\n # \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc640 \ub808\uc774\ube14 \uc0dd\uc131\n real_imgs = imgs\n real_labels = torch.ones(imgs.size(0), 1)\n \n # \uac00\uc9dc \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubc0f \ub808\uc774\ube14 \uc0dd\uc131\n noise = torch.randn(imgs.size(0), 100)\n fake_imgs = generator(noise)\n fake_labels = torch.zeros(imgs.size(0), 1)\n\n # \uad6c\ubd84\uc790 \uc5c5\ub370\uc774\ud2b8\n optimizer_D.zero_grad()\n outputs = discriminator(real_imgs)\n d_loss_real = criterion(outputs, real_labels)\n d_loss_real.backward()\n\n outputs = discriminator(fake_imgs.detach())\n d_loss_fake = criterion(outputs, fake_labels)\n d_loss_fake.backward()\n optimizer_D.step()\n\n # \uc0dd\uc131\uc790 \uc5c5\ub370\uc774\ud2b8\n optimizer_G.zero_grad()\n outputs = discriminator(fake_imgs)\n g_loss = criterion(outputs, real_labels)\n g_loss.backward()\n optimizer_G.step()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item()}, g_loss: {g_loss.item()}')\n\n if (epoch + 1) % 20 == 0:\n with torch.no_grad():\n fake_imgs = generator(noise)\n plt.imshow(fake_imgs[0][0].cpu().numpy(), cmap='gray')\n plt.show()\n <\/pre>\n4. GAN\uc758 \ud6c8\ub828 \uc911 \uc9c1\uba74\ud558\ub294 \ub09c\uad00<\/h2>\n
\n GAN \ud6c8\ub828 \uacfc\uc815 \uc911\uc5d0\ub294 \uc5ec\ub7ec \uac00\uc9c0 \ub09c\uad00\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uadf8 \uc911 \uba87 \uac00\uc9c0 \uc8fc\uc694 \ubb38\uc81c\uc640 \ud574\uacb0 \ubc29\ubc95\uc744 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.1 \ubaa8\ub4dc \ubd95\uad34 (Mode Collapse)<\/h3>\n
\n \ubaa8\ub4dc \ubd95\uad34\ub780 \uc0dd\uc131\uc790\uac00 \ube60\ub974\uac8c \uad6c\ubd84\uc790\ub97c \uc18d\uc774\uae30 \ub54c\ubb38\uc5d0 \uc778\uc9c0\uc801 \ub2e4\uc591\uc131\uc774 \uc5c6\ub294 \ub3d9\uc77c\ud55c \uc774\ubbf8\uc9c0\ub9cc \uc0dd\uc131\ud558\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc774\ub294 GAN\uc758 \ud070 \ubb38\uc81c \uc911 \ud558\ub098\ub85c, \uc0dd\uc131\uc790\uc758 \ub2e4\uc591\uc131\uc744 \ubc29\ud574\ud558\uba70 \ud488\uc9c8 \uc788\ub294 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ubc29\ud574\uac00 \ub429\ub2c8\ub2e4.\n <\/p>\n
\n \uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uae30\ubc95\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc591\ud55c \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131\uc790\uc758 \ub2e4\uc591\uc131\uc744 \ub298\ub9ac\uac70\ub098, \uad6c\ubd84\uc790\uc758 \uad6c\uc870\ub97c \ubcf5\uc7a1\ud558\uac8c \ud558\uc5ec \ubaa8\ub4dc \ubd95\uad34\ub97c \ubc29\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.2 \ube44\uc18c\uc2e4 (Non-convergence)<\/h3>\n
\n GAN\uc740 \uc885\uc885 \ud6c8\ub828\uc774 \ubd88\uc548\uc815\ud558\uc5ec \uc218\ub834\ud558\uc9c0 \uc54a\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc704\uc5d0\uc11c \ubcf4\uc558\ub358 \uc190\uc2e4 \ud568\uc218\uc758 \uac12\uc774 \uc9c0\uc18d\uc801\uc73c\ub85c \ubcc0\ub3d9\ud558\uac70\ub098, \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790\uac00 \uacf5\uc874\ud558\uc9c0 \ubabb\ud558\ub294 \uc0c1\ud669\uc744 \ucd08\ub798\ud569\ub2c8\ub2e4. \uc774\ub294 \ud559\uc2b5\ub960\uacfc \ubc30\uce58 \ud06c\uae30\ub97c \uc870\uc815\ud558\uac70\ub098, \uc5ec\ub7ec \ub2e8\uacc4\uc758 \ud6c8\ub828 \uc870\uc815\uc744 \ud1b5\ud574 \ud574\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.3 \ubd88\uade0\ud615 (Unbalanced Training)<\/h3>\n
\n \ubd88\uade0\ud615\ud55c \ud6c8\ub828\uc740 \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790\uac00 \ub3d9\uc2dc\uc5d0 \ud6c8\ub828\ub420 \ub54c \ud55c\ucabd\uc774 \ub2e4\ub978 \ucabd\ubcf4\ub2e4 \uc6b0\uc138\ud558\uac8c \ud559\uc2b5\ub420 \uc218 \uc788\ub294 \ubb38\uc81c\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uad6c\ubd84\uc790\uac00 \ub108\ubb34 \uac15\ub825\ud558\uac8c \ud559\uc2b5\ub418\uba74 \uc0dd\uc131\uc790\ub294 \uadf9\ubcf5\ud560 \uc218 \uc5c6\ub294 \uc0c1\ud669\uc774 \ub418\uc5b4 \ud559\uc2b5\uc744 \ud3ec\uae30\ud558\uac8c \ub429\ub2c8\ub2e4. \uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \uc8fc\uae30\uc801\uc73c\ub85c \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790\ub97c \ub530\ub85c \uac31\uc2e0\ud558\uac70\ub098, \ud658\uacbd\uc5d0 \ub530\ub77c \uc190\uc2e4 \ud568\uc218 \ub610\ub294 \ud559\uc2b5\ub960\uc744 \uc870\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
5. GAN\uc758 \ubc1c\uc804 \ubc29\ud5a5<\/h2>\n
\n \ucd5c\uadfc GAN \uae30\uc220\uc740 \ub300\ud3ed \ubc1c\uc804\ud558\uc5ec \ub2e4\uc591\ud55c \ubcc0\ud615 \ubaa8\ub378\uc774 \ub4f1\uc7a5\ud558\uc600\uc2b5\ub2c8\ub2e4. DCGAN(Deep Convolutional GAN), WGAN(Wasserstein GAN) \ubc0f StyleGAN \ub4f1\uc774 \uc774\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc740 GAN\uc758 \uae30\uc874 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uace0 \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n <\/p>\n
5.1 DCGAN<\/h3>\n
\n DCGAN\uc740 CNN(Convolutional Neural Network)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c GAN \uad6c\uc870\ub85c, \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \ub370\uc5d0 \ud6e8\uc52c \ub354 \ud6a8\uc728\uc801\uc785\ub2c8\ub2e4. \uc774 \uad6c\uc870\ub294 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc758 \ud488\uc9c8\uc744 \ud06c\uac8c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.\n <\/p>\n
5.2 WGAN<\/h3>\n
\n WGAN\uc740 Wasserstein \uac70\ub9ac \uac1c\ub150\uc744 \uc0ac\uc6a9\ud558\uc5ec GAN \ud6c8\ub828\uc758 \uc548\uc815\uc131\uacfc \uc131\ub2a5\uc744 \ud06c\uac8c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4. WGAN\uc740 \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790 \uac04\uc758 \uac70\ub9ac\ub97c \ubcf4\uc874\ud558\uc5ec \ud559\uc2b5\uc758 \uc548\uc815\uc131\uc744 \ubcf4\uc7a5\ud569\ub2c8\ub2e4.\n <\/p>\n
5.3 StyleGAN<\/h3>\n
\n StyleGAN\uc740 \uc2a4\ud0c0\uc77c \uc804\uc774(Style Transfer) \uac1c\ub150\uc744 \ub3c4\uc785\ud558\uc5ec \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ub192\uc740 \ud488\uc9c8\uc744 \uc720\uc9c0\ud558\uba74\uc11c \ub2e4\uc591\ud55c \uc2a4\ud0c0\uc77c\uc744 \ud559\uc2b5\uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. ImageNet \ub370\uc774\ud130\uc14b\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc5d0\uc11c\ub294 \ud2b9\ud788 \ub450\uac01\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n <\/p>\n
\uacb0\ub860<\/h2>\n
\n GAN\uc740 \ub370\uc774\ud130 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uc131\uacfc\ub97c \uc774\ub8e8\uc5b4\ub0b8 \uc911\uc694\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \ud1b5\ud574 GAN\uc744 \uad6c\ud604\ud568\uc73c\ub85c\uc368 \uc0dd\uc131 \ubaa8\ub378\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc775\ud790 \uc218 \uc788\uc73c\uba70, \uc5ec\ub7ec \uac00\uc9c0 \ubb38\uc81c\uc810\ub4e4\uc744 \uc774\ud574\ud558\uace0 \uc774\ub97c \uadf9\ubcf5\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ubc1c\uc804\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \ud5a5\ud6c4 GAN \uae30\uc220\uc774 \ub354\uc6b1 \ubc1c\uc804\ud558\uc5ec \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uae30\ub97c \uae30\ub300\ud569\ub2c8\ub2e4. GAN\uc744 \ud65c\uc6a9\ud55c \uc5f0\uad6c\uc640 \uac1c\ubc1c\uc740 \uacc4\uc18d\ud574\uc11c \uc774\uc5b4\uc9c8 \uac83\uc774\uba70, \uc0c8\ub85c\uc6b4 \uc811\uadfc \ubc29\uc2dd\uc744 \ud1b5\ud574 \uc55e\uc73c\ub85c \ud070 \uac00\ub2a5\uc131\uc744 \uc5f4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n<\/article>\n
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