{"id":29879,"date":"2024-10-28T03:00:42","date_gmt":"2024-10-28T03:00:42","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29879"},"modified":"2024-11-26T06:51:00","modified_gmt":"2024-11-26T06:51:00","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-%ed%8a%b8%eb%9e%9c%ec%8a%a4%ed%8f%ac%eb%a8%b8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29879\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud2b8\ub79c\uc2a4\ud3ec\uba38"},"content":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc544\ud2f0\uc2a4\ud2b8, \uc5f0\uad6c\uc790, \uac1c\ubc1c\uc790 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\ucce4\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GAN)\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \uc544\ud0a4\ud14d\ucc98\ub294 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \uc774 \ub450 \uae30\uc220\uc758 \uacb0\ud569\uc740 \ub180\ub77c\uc6b4 \uacb0\uacfc\ub97c \ub9cc\ub4e4\uc5b4 \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. GAN\uc758 \uae30\ucd08<\/h2>\n<p>GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uba70, \ud310\ubcc4\uae30\ub294 \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc640 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uad6c\ubcc4\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70, \uacb0\uad6d \uc0dd\uc131\uae30\ub294 \uc810\uc810 \ub354 \ud604\uc2e4\uc801\uc778 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uc5b4\ub0b4\uac8c \ub429\ub2c8\ub2e4.<\/p>\n<h3>1.1 GAN\uc758 \ub3d9\uc791 \uc6d0\ub9ac<\/h3>\n<p>GAN\uc758 \ud6c8\ub828 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ol>\n<li>\ub79c\ub364 \ub178\uc774\uc988\ub97c \uae30\ubc18\uc73c\ub85c \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud55c\ub2e4.<\/li>\n<li>\uc0dd\uc131\ub41c \uac00\uc9dc \uc774\ubbf8\uc9c0\uc640 \uc2e4\uc81c \uc774\ubbf8\uc9c0\ub97c \ud310\ubcc4\uae30\uc5d0 \uc785\ub825\ud55c\ub2e4.<\/li>\n<li>\ud310\ubcc4\uae30\ub294 \ub450 \uc774\ubbf8\uc9c0\uc758 \uc9c4\uc704\ub97c \ud310\ub2e8\ud558\uace0, \uac01 \uc774\ubbf8\uc9c0\ub97c \uc9c4\uc9dc(1) \ub610\ub294 \uac00\uc9dc(0)\ub85c \ub77c\ubca8\ub9c1 \ud55c\ub2e4.<\/li>\n<li>\ud310\ubcc4\uae30\uc758 \ucd9c\ub825\uc744 \uae30\uc900\uc73c\ub85c \uc0dd\uc131\uae30\uc758 \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0 \uc774\ub97c \uc774\uc6a9\ud574 \uc0dd\uc131\uae30\ub97c \uc5c5\ub370\uc774\ud2b8\ud55c\ub2e4.<\/li>\n<li>\uc774 \uacfc\uc815\uc744 \ubc18\ubcf5\ud558\uc5ec \uc0dd\uc131\uae30\ub294 \uc810\uc810 \ub354 \uc9c4\uc9dc \uac19\uc740 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uac8c \ub41c\ub2e4.<\/li>\n<\/ol>\n<h3>1.2 GAN \uad6c\ud604\ud558\uae30<\/h3>\n<p>\ub2e4\uc74c\uc740 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uc744 \uad6c\ud604\ud558\ub294 \uae30\ubcf8\uc801\uc778 \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\n\n# Hyperparameters\nlatent_size = 64\nbatch_size = 128\nlearning_rate = 0.0002\nnum_epochs = 50\n\n# Device configuration\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# Load MNIST dataset\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\nmnist = torchvision.datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\ndata_loader = torch.utils.data.DataLoader(mnist, batch_size=batch_size, shuffle=True)\n\n# Create the Generator model\nclass Generator(nn.Module):\n    def __init__(self):\n        super(Generator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(latent_size, 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# Create the Discriminator model\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Flatten(),\n            nn.Linear(784, 1024),\n            nn.LeakyReLU(0.2),\n            nn.Linear(1024, 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)\n\n# Initialize the models\ngenerator = Generator().to(device)\ndiscriminator = Discriminator().to(device)\n\n# Loss and optimizer\ncriterion = nn.BCELoss()\noptimizer_g = optim.Adam(generator.parameters(), lr=learning_rate)\noptimizer_d = optim.Adam(discriminator.parameters(), lr=learning_rate)\n\n# Training the GAN\nfor epoch in range(num_epochs):\n    for i, (imgs, _) in enumerate(data_loader):\n        # Configure input\n        imgs = imgs.to(device)\n        batch_size = imgs.size(0)\n\n        # Labels for real and fake images\n        real_labels = torch.ones(batch_size, 1).to(device)\n        fake_labels = torch.zeros(batch_size, 1).to(device)\n\n        # Train the Discriminator\n        optimizer_d.zero_grad()\n        outputs = discriminator(imgs)\n        d_loss_real = criterion(outputs, real_labels)\n\n        z = torch.randn(batch_size, latent_size).to(device)\n        fake_imgs = generator(z)\n        outputs = discriminator(fake_imgs.detach())\n        d_loss_fake = criterion(outputs, fake_labels)\n\n        d_loss = d_loss_real + d_loss_fake\n        d_loss.backward()\n        optimizer_d.step()\n\n        # Train the Generator\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.item():.4f}, g_loss: {g_loss.item():.4f}')\n\n# Save generated images from the generator\n<\/code><\/pre>\n<h2>2. \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \uae30\ucd08<\/h2>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubc0f \ub2e4\ub978 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \ubaa8\ub378\ub85c, \ub370\uc774\ud130\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \uc2dc\ud000\uc2a4 \uae38\uc774\uc5d0 \uc0c1\uad00\uc5c6\uc774 \ubcd1\ub82c \ucc98\ub9ac\uac00 \uac00\ub2a5\ud558\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc758 \ud575\uc2ec\uc740 \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998(Attention Mechanism)\uc785\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \uad6c\uc131 \uc694\uc18c<\/h3>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \uc785\ub825 \uc778\ucf54\ub354(Encoder)\uc640 \ucd9c\ub825 \ub514\ucf54\ub354(Decoder)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc778\ucf54\ub354\ub294 \uc785\ub825\uc744 \ubc1b\uc544 \uc815\ubcf4\ub97c \ucc98\ub9ac\ud558\uace0, \ub514\ucf54\ub354\ub294 \uc778\ucf54\ub354\uc758 \ucd9c\ub825\uc744 \uae30\ubc18\uc73c\ub85c \ucd5c\uc885 \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n<h3>2.2 \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998<\/h3>\n<p>\uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998\uc740 \uc785\ub825 \ub370\uc774\ud130\uc758 \uc911\uc694\ub3c4\ub97c \ud3c9\uac00\ud558\uc5ec \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc785\ub825\uc758 \ubaa8\ub4e0 \ubd80\ubd84\uc5d0 \uc8fc\uc758\ub97c \uae30\uc6b8\uc5ec\uc57c \ud558\ub294 \uacbd\uc6b0\uc5d0 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h3>2.3 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \uad6c\ud604\ud558\uae30<\/h3>\n<p>\ub2e4\uc74c\uc740 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc744 \uad6c\ud604\ud55c \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>class MultiHeadAttention(nn.Module):\n    def __init__(self, embed_size, heads):\n        super(MultiHeadAttention, self).__init__()\n        self.embed_size = embed_size\n        self.heads = heads\n        self.head_dim = embed_size \/\/ heads\n\n        assert (\n            self.head_dim * heads == embed_size\n        ), \"Embedding size needs to be divible by heads\"\n\n        self.values = nn.Linear(embed_size, embed_size, bias=False)\n        self.keys = nn.Linear(embed_size, embed_size, bias=False)\n        self.queries = nn.Linear(embed_size, embed_size, bias=False)\n        self.fc_out = nn.Linear(embed_size, embed_size)\n\n    def forward(self, query, key, value, mask):\n        N = query.shape[0]\n        value_len, key_len, query_len = value.shape[1], key.shape[1], query.shape[1]\n\n        # Split the embedding into multiple heads\n        value = self.values(value).view(N, value_len, self.heads, self.head_dim)\n        key = self.keys(key).view(N, key_len, self.heads, self.head_dim)\n        query = self.queries(query).view(N, query_len, self.heads, self.head_dim)\n\n        # Transpose to get dimensions N x heads x query_len x head_dim\n        value = value.permute(0, 2, 1, 3)  # N x heads x value_len x head_dim\n        key = key.permute(0, 2, 1, 3)      # N x heads x key_len x head_dim\n        query = query.permute(0, 2, 1, 3)  # N x heads x query_len x head_dim\n\n        # Calculate the energy scores\n        energy = torch.einsum(\"nqhd,nkhd-&gt;nqkh\", [query, key])\n\n        if mask is not None:\n            energy += (mask * -1e10)\n\n        attention = torch.softmax(energy, dim=3)\n\n        # Weighted sum of the values\n        out = torch.einsum(\"nqkh,nvhd-&gt;nqhd\", [attention, value]).reshape(\n            N, query_len, self.heads * self.head_dim\n        )\n\n        return self.fc_out(out)\n\n# For complete transformer implementation, we would add the Encoder, Decoder, and complete model as well.\n<\/code><\/pre>\n<h2>3. GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \ud1b5\ud569<\/h2>\n<p>GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\uc758 \ud1b5\ud569\uc740 \uc5ec\ub7ec \uc0c8\ub85c\uc6b4 \uac00\ub2a5\ud55c \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \uc81c\uc2dc\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c GAN\uc758 \uc0dd\uc131\uae30 \ub610\ub294 \ud310\ubcc4\uae30\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc811\uadfc\ubc95\uc740 \ud2b9\ud788 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \uacbd\uc6b0 \uc720\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ud2b8\ub79c\uc2a4\ud3ec\uba38 GAN<\/h3>\n<p>GAN\uc758 \uc0dd\uc131\uae30 \ub300\uc2e0 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uc0ac\uc6a9\ud558\uba74 \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\uc758 \uad6c\uc870\ub97c \ubaa8\ub378\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc5d0 \ud6a8\uacfc\uc801\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.2 \uc2e4\uc81c \uc608\uc81c: \ud2b8\ub79c\uc2a4\ud3ec\uba38 GAN \uad6c\ud604<\/h3>\n<p>\ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c GAN\uc5d0 \ud1b5\ud569\ud55c \ubaa8\ub378\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>class TransformerGenerator(nn.Module):\n    def __init__(self):\n        super(TransformerGenerator, self).__init__()\n        # Define your transformer architecture here\n\n    def forward(self, z):\n        # Define forward pass\n        return transformed_output\n\nclass TransformerDiscriminator(nn.Module):\n    def __init__(self):\n        super(TransformerDiscriminator, self).__init__()\n        # Define your discriminator architecture here\n\n    def forward(self, img):\n        # Define forward pass\n        return discriminator_output\n<\/code><\/pre>\n<h2>4. \uacb0\ub860<\/h2>\n<p>\uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uc774\uba70, \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \ub370\uc774\ud130\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4. \ub450 \uae30\uc220\uc758 \uacb0\ud569\uc740 \ub354 \ub192\uc740 \ud488\uc9c8\uc758 \ub370\uc774\ud130 \uc0dd\uc131\uc73c\ub85c \uc774\uc5b4\uc9c8 \uc218 \uc788\uc73c\uba70, \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c\uc758 \ud601\uc2e0\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \ucd94\uc9c4\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<p>\uc5ec\ub7ec\ubd84\uc774 \uc81c\uacf5\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc2e4\uc81c\ub85c GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uad6c\ud604\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \ub354 \ub9ce\uc740 \uc2e4\ud5d8\uacfc \uc5f0\uad6c\ub97c \ud1b5\ud574 \ub354\uc6b1 \ubc1c\uc804\ub41c \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ub098\uac00\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n<h2>\ucc38\uace0 \uc790\ub8cc<\/h2>\n<ul>\n<li>Ian Goodfellow et al., &#8220;Generative Adversarial Networks&#8221;, 2014.<\/li>\n<li>Ashish Vaswani et al., &#8220;Attention is All You Need&#8221;, 2017.<\/li>\n<li>PyTorch Documentation: https:\/\/pytorch.org\/docs\/stable\/index.html<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc544\ud2f0\uc2a4\ud2b8, \uc5f0\uad6c\uc790, \uac1c\ubc1c\uc790 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\ucce4\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GAN)\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \uc544\ud0a4\ud14d\ucc98\ub294 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \uc774 \ub450 \uae30\uc220\uc758 \uacb0\ud569\uc740 \ub180\ub77c\uc6b4 \uacb0\uacfc\ub97c \ub9cc\ub4e4\uc5b4 \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. GAN\uc758 \uae30\ucd08 GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29879\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud2b8\ub79c\uc2a4\ud3ec\uba38&#8221;<\/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-29879","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud2b8\ub79c\uc2a4\ud3ec\uba38 - \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\/29879\/\" \/>\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, \ud2b8\ub79c\uc2a4\ud3ec\uba38 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc544\ud2f0\uc2a4\ud2b8, \uc5f0\uad6c\uc790, \uac1c\ubc1c\uc790 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\ucce4\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GAN)\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer) \uc544\ud0a4\ud14d\ucc98\ub294 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \uc774 \ub450 \uae30\uc220\uc758 \uacb0\ud569\uc740 \ub180\ub77c\uc6b4 \uacb0\uacfc\ub97c \ub9cc\ub4e4\uc5b4 \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uacfc \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. 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