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\uac15\ub825\ud55c \ub2a5\ub825\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%ec%9e%90%eb%8f%99-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25493,"url":"https:\/\/atmokpo.com\/w\/25493\/","url_meta":{"origin":29820,"position":4},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc9c8\uc758 \uc751\ub2f5(Question Answering, QA)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc790: \uc870\uad11\ud615 | \ub0a0\uc9dc: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \ucef4\ud4e8\ud130 \uacfc\ud559\uc758 \ud55c \ubd84\uc57c\ub85c, \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4. \ud2b9\ud788, \uc9c8\uc758 \uc751\ub2f5 \uc2dc\uc2a4\ud15c\uc740 \uc0ac\uc6a9\uc790\ub85c\ubd80\ud130 \uc790\uc5f0\uc5b4\ub85c \uc785\ub825\ub41c \uc9c8\ubb38\uc744 \uc774\ud574\ud558\uace0, \uadf8\uc5d0 \ub300\ud55c \uc801\uc808\ud55c \ub2f5\ubcc0\uc744 \uc81c\uacf5\ud558\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25269,"url":"https:\/\/atmokpo.com\/w\/25269\/","url_meta":{"origin":29820,"position":5},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud53c\ub4dc \ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd \uc5b8\uc5b4 \ubaa8\ub378 (Neural Network Language Model, NNLM)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 \ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ub525 \ub7ec\ub2dd(deep learning)\uc758 \ubc1c\uc804\uc740 \ub9e4\uc6b0 \ub208\ubd80\uc2e0 \ubcc0\ud654\uc640 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd\uc740 \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP) \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc73c\uba70, \uadf8\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ubaa8\ub378\ub4e4\uc774 \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud53c\ub4dc \ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd \uc5b8\uc5b4 \ubaa8\ub378(Neural Network Language\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd 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{"id":29820,"date":"2024-10-28T03:00:22","date_gmt":"2024-10-28T03:00:22","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29820"},"modified":"2024-11-26T06:51:14","modified_gmt":"2024-11-26T06:51:14","slug":"%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98%eb%a5%bc-%ed%99%9c%ec%9a%a9%ed%95%9c-gan-%eb%94%a5%eb%9f%ac%eb%8b%9d-wgan-%ec%99%80%ec%84%9c%ec%8a%a4%ed%85%8c%ec%9d%b8-gan","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29820\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN – \uc640\uc11c\uc2a4\ud14c\uc778 GAN"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uac15\ud654\ud559\uc2b5, \uc774\ubbf8\uc9c0 \ubcc0\ud658, \uc774\ubbf8\uc9c0 \uacb0\ud569 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c Generative Adversarial Networks(GANs)\uc758 \uc0ac\uc6a9\uc774 \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub450 \ub124\ud2b8\uc6cc\ud06c, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator) \uac04\uc758 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uace0\ud574\uc0c1\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294\ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud568\uaed8 WGAN(Wasserstein GAN)\uc758 \uad6c\uc870\uc640 \uc791\ub3d9 \ubc29\uc2dd, \uc774\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud55c \ud30c\uc774\ud1a0\uce58 \uc608\uc81c \ucf54\ub4dc\ub97c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
GAN\uc740 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \ubaa8\ub378\ub85c, \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub77c\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ub09c\uc218 \ubca1\ud130\ub97c \uc785\ub825\ubc1b\uc544 \uc2e4\uc81c\uc640 \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc785\ub825 \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \ud310\ub2e8\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ub450 \uc2e0\uacbd\ub9dd\uc740 \uc11c\ub85c \uacbd\uc7c1\ud558\uba74\uc11c \uc810\uc810 \uc644\ubcbd\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

1.1 GAN\uc758 \uad6c\uc870<\/h3>\n

Generator (G)<\/strong>: \ub79c\ub364 \ub178\uc774\uc988\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub124\ud2b8\uc6cc\ud06c\uc785\ub2c8\ub2e4.<\/li>\n

Discriminator (D)<\/strong>: \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc0dd\uc131\ub41c \ub370\uc774\ud130 \uac04\uc758 \ucc28\uc774\ub97c \uad6c\ubcc4\ud558\ub294 \ub124\ud2b8\uc6cc\ud06c\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n
1.2 GAN\uc758 \uc190\uc2e4 \ud568\uc218<\/h3>\n
GAN\uc758 \uc190\uc2e4 \ud568\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\n L(D) = -E[log(D(x))] - E[log(1 - D(G(z)))],\n L(G) = -E[log(D(G(z)))]\n <\/pre>\n
\uc5ec\uae30\uc11c D(x)<\/code>\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc758 \uc9c4\uc9dc\ub85c \ud310\ub2e8\ud560 \ud655\ub960\uc774\uba70, G(z)<\/code>\ub294 \uc0dd\uc131\uc790\uac00 \uc0dd\uc131\ud55c \ub370\uc774\ud130\uc785\ub2c8\ub2e4.<\/p>\n
2. WGAN – \uc640\uc11c\uc2a4\ud14c\uc778 GAN<\/h2>\n\uae30\uc874\uc758 GAN\uc740 \ud310\ubcc4\uc790\uc758 \uc190\uc2e4 \ud568\uc218\uac00 \uc548\uc815\uc801\uc774\uc9c0 \uc54a\uace0 \ud559\uc2b5\uc774 \ubd88\uc548\uc815\ud558\ub2e4\ub294 \ubb38\uc81c\uc810\uc774 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. WGAN\uc740 Wasserstein Distance\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \ubb38\uc81c\uc810\uc744 \ud574\uacb0\ud569\ub2c8\ub2e4. Wasserstein \uac70\ub9ac(\ud639\uc740 Earth Mover’s Distance)\ub294 \ub450 \ud655\ub960 \ubd84\ud3ec \uac04\uc758 \ucd5c\uc801 \uc6b4\ubc18 \ube44\uc6a9\uc744 \uce21\uc815\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n 2.1 WGAN\uc758 \uac1c\uc120\uc810<\/h3>\n\nWGAN\uc740 \ud310\ubcc4\uc790 \ub300\uc2e0 \ube44\uc120\ud615 \ud68c\uadc0\ubaa8\ub378\uc778 ‘Critic’\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n WGAN\uc758 \uc190\uc2e4 \ud568\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n L(D) = E[D(x)] - E[D(G(z))],\n L(G) = -E[D(G(z))]\n <\/pre>\n<\/li>\nWGAN\uc740 Weight Clipping\uc744 \ud1b5\ud574 Critic\uc758 Lipschitz \uc5f0\uc18d\uc131\uc744 \ubcf4\uc7a5\ud569\ub2c8\ub2e4.<\/li>\n Gradient Penalty \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec Lipschitz \uc81c\uc57d \uc870\uac74\uc744 \uc644\ud654\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n2.2 WGAN\uc758 \uad6c\uc870<\/h3>\nWGAN\uc740 \uae30\ubcf8 GAN\uc758 \uad6c\uc870\uc5d0\uc11c Critic\uc744 \ub3c4\uc785\ud574 \uc218\uc815\ub41c \ud615\ud0dc\uc785\ub2c8\ub2e4. \ub2e4\uc74c\uc740 WGAN\uc758 \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870\uc785\ub2c8\ub2e4:<\/p>\n \n\uc774\uc804\uc758 \ud310\ubcc4\uc790\ub294 \ud604\uc7ac\uc758 Critic\uc73c\ub85c \ub300\uccb4\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n3. \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c WGAN \uad6c\ud604<\/h2>\n\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub85c WGAN\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc0dd\uc131\ud558\ub294\ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n 3.1 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n\uba3c\uc800 \ub370\uc774\ud130\uc14b\uc744 \ubd88\ub7ec\uc624\uace0 \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n \nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uace0 \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4.\ntransform = transforms.Compose([\n transforms.Resize(28),\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\ntrain_data = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = DataLoader(train_data, batch_size=64, shuffle=True)\n <\/code><\/pre>\n3.2 WGAN \ubaa8\ub378 \uc815\uc758<\/h3>\n\uc774\uc81c Generator\uc640 Critic \ubaa8\ub378\uc744 \uc815\uc758\ud560 \ucc28\ub840\uc785\ub2c8\ub2e4.<\/p>\n \n# \uc0dd\uc131\uc790 \ubaa8\ub378 \uc815\uc758\nclass 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) # 28x28 \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud615\n\n# \ube44\ud3c9\uac00(critic) \ubaa8\ub378 \uc815\uc758\nclass Critic(nn.Module):\n def __init__(self):\n super(Critic, 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 )\n \n def forward(self, img):\n return self.model(img.view(-1, 784)) # 784 \ucc28\uc6d0\uc73c\ub85c \ubcc0\ud615\n <\/code><\/pre>\n3.3 WGAN \ud559\uc2b5 \uacfc\uc815<\/h3>\n\uc774\uc81c WGAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n \ndef train_wgan(num_epochs):\n generator = Generator()\n critic = Critic()\n \n # \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\n optimizer_G = optim.RMSprop(generator.parameters(), lr=0.00005)\n optimizer_C = optim.RMSprop(critic.parameters(), lr=0.00005)\n\n for epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(train_loader):\n imgs = imgs.to(device)\n\n # Critic \uc794\uc5ec \ubc29\uc815\uc2dd\n optimizer_C.zero_grad()\n z = torch.randn(imgs.size(0), 100).to(device)\n fake_imgs = generator(z)\n c_real = critic(imgs)\n c_fake = critic(fake_imgs.detach())\n c_loss = c_fake.mean() - c_real.mean()\n c_loss.backward()\n optimizer_C.step()\n\n # Weight Clipping\n for p in critic.parameters():\n p.data.clamp_(-0.01, 0.01)\n\n # Generator \uc5c5\ub370\uc774\ud2b8\n if i % 5 == 0:\n optimizer_G.zero_grad()\n g_loss = -critic(fake_imgs).mean()\n g_loss.backward()\n optimizer_G.step()\n \n print(f'Epoch [{epoch}\/{num_epochs}], Loss C: {c_loss.item()}, Loss G: {g_loss.item()}')\n\n# GPU \uc0ac\uc6a9 \uc124\uc815\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ntrain_wgan(num_epochs=50)\n <\/code><\/pre>\n3.4 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n\ud559\uc2b5\uc774 \ub05d\ub098\uba74 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \uacb0\uacfc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4.<\/p>\n \nimport matplotlib.pyplot as plt\n\ndef show_generated_images(num_images):\n z = torch.randn(num_images, 100).to(device)\n generated_imgs = generator(z).cpu().detach()\n \n fig, axes = plt.subplots(1, num_images, figsize=(15, 15))\n for i in range(num_images):\n axes[i].imshow(generated_imgs[i][0], cmap='gray')\n axes[i].axis('off')\n plt.show()\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nshow_generated_images(5)\n <\/code><\/pre>\n4. \uacb0\ub860<\/h2>\nWGAN\uc740 \uae30\uc874 GAN\uc758 \ubb38\uc81c\uc810\uc744 \uadf9\ubcf5\ud558\uae30 \uc704\ud574 Wasserstein Distance\ub97c \uc774\uc6a9\ud558\uc5ec \ub354 \uc548\uc815\uc801\uc778 \ud559\uc2b5 \uacfc\uc815\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec WGAN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud558\uc600\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd\uc758 \uc774\ud574\ub97c \ub192\uc77c \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. GAN\uacfc \uadf8 \ubcc0\ud615 \ubaa8\ub378\ub4e4\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131\ubfd0\ub9cc \uc544\ub2c8\ub77c \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uacb0\uacfc\ub97c \uac00\uc838\uc62c \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4.<\/p>\n 5. \ucc38\uace0\ubb38\ud5cc<\/h2>\n\nIan J. Goodfellow et al., “Generative Adversarial Nets”, 2014.<\/li>\n Martin Arjovsky et al., “Wasserstein Generative Adversarial Networks”, 2017.<\/li>\n PyTorch Documentation: https:\/\/pytorch.org\/docs\/stable\/index.html<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uac15\ud654\ud559\uc2b5, \uc774\ubbf8\uc9c0 \ubcc0\ud658, \uc774\ubbf8\uc9c0 \uacb0\ud569 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c Generative Adversarial Networks(GANs)\uc758 \uc0ac\uc6a9\uc774 \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub450 \ub124\ud2b8\uc6cc\ud06c, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator) \uac04\uc758 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uace0\ud574\uc0c1\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294\ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud568\uaed8 WGAN(Wasserstein GAN)\uc758 \uad6c\uc870\uc640 \uc791\ub3d9 \ubc29\uc2dd, \uc774\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud55c \ud30c\uc774\ud1a0\uce58 \uc608\uc81c \ucf54\ub4dc\ub97c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. 1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150 GAN\uc740 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN – \uc640\uc11c\uc2a4\ud14c\uc778 GAN”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[32],"tags":[],"class_list":["post-29820","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"\n\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN - \uc640\uc11c\uc2a4\ud14c\uc778 GAN - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29820\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN - \uc640\uc11c\uc2a4\ud14c\uc778 GAN - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uac15\ud654\ud559\uc2b5, \uc774\ubbf8\uc9c0 \ubcc0\ud658, \uc774\ubbf8\uc9c0 \uacb0\ud569 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c Generative Adversarial Networks(GANs)\uc758 \uc0ac\uc6a9\uc774 \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub450 \ub124\ud2b8\uc6cc\ud06c, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator) \uac04\uc758 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uace0\ud574\uc0c1\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294\ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud568\uaed8 WGAN(Wasserstein GAN)\uc758 \uad6c\uc870\uc640 \uc791\ub3d9 \ubc29\uc2dd, \uc774\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud55c \ud30c\uc774\ud1a0\uce58 \uc608\uc81c \ucf54\ub4dc\ub97c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. 1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150 GAN\uc740 … \ub354 \ubcf4\uae30 "\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN – \uc640\uc11c\uc2a4\ud14c\uc778 GAN"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29820\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:00:22+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:51:14+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29820\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29820\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN – \uc640\uc11c\uc2a4\ud14c\uc778 GAN\",\"datePublished\":\"2024-10-28T03:00:22+00:00\",\"dateModified\":\"2024-11-26T06:51:14+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29820\/\"},\"wordCount\":95,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"GAN \ub525\ub7ec\ub2dd \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29820\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29820\/\",\"name\":\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN - 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