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이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
256 lines
7.6 KiB
Markdown
256 lines
7.6 KiB
Markdown
---
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id: wiki-2026-0508-dcgan
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title: DCGAN (Deep Convolutional GAN)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [DCGAN, GAN, generative adversarial network, StyleGAN, CycleGAN, Pix2Pix]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [gan, dcgan, generative-models, deep-learning, image-generation, stylegan, cyclegan, history]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: PyTorch
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---
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# DCGAN
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## 매 한 줄
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> **"매 GAN 의 first 의 stable architecture"** (Radford 2015). 매 stride conv + 매 batch norm + 매 specific activation. 매 generative AI 의 grandparent. 매 modern: 매 Diffusion 의 superseded 가, 매 fast inference / GAN-based super-res / image-to-image 의 still relevant.
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## 매 핵심
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### 매 GAN basics (Goodfellow 2014)
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- **Generator**: 매 noise → 매 image.
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- **Discriminator**: 매 real vs fake.
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- **Min-max game**.
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### DCGAN (2015) 의 contribution
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1. **Strided conv** (no pooling).
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2. **BatchNorm** in both G and D.
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3. **No fully-connected hidden** layer.
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4. **ReLU in G** (Tanh output).
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5. **LeakyReLU in D**.
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### 매 famous GAN evolution
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- **DCGAN** (2015): 매 stable.
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- **WGAN** (2017): 매 Wasserstein loss, 매 mode collapse 의 mitigate.
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- **Pix2Pix** (2017): 매 image-to-image.
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- **CycleGAN** (2017): 매 unpaired.
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- **StyleGAN** (2018-2021): 매 face quality SOTA.
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- **BigGAN** (2018): 매 large-scale.
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- **GigaGAN** (2023): 매 text-to-image GAN.
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### 매 mode collapse
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- 매 G 의 매 limited variety 의 generate.
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- 매 mitigation: 매 minibatch discrimination, 매 spectral norm, 매 WGAN.
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### 매 evaluation
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- **FID** (Fréchet Inception Distance): 매 generated vs real 의 distance.
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- **IS** (Inception Score).
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- **Precision / Recall** (Kynkäänniemi 2019).
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### 매 modern relevance
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- **Super-resolution** (ESRGAN, Real-ESRGAN).
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- **Image-to-image** (CycleGAN 의 still useful).
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- **Domain adaptation** (sim2real).
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- **Implicit EBM**.
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- **Fast generation** (vs slow diffusion).
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### 매 vs Diffusion
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| 측면 | GAN | Diffusion |
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|---|---|---|
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| Quality | High (StyleGAN) | Highest |
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| Diversity | Mode collapse risk | High |
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| Training stability | Tricky | Stable |
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| Inference speed | Fast (single step) | Slow (multi-step) |
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| Conditioning | Hard | Easy (CLIP) |
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## 💻 패턴
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### DCGAN (PyTorch)
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```python
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import torch.nn as nn
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class Generator(nn.Module):
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def __init__(self, nz=100, ngf=64, nc=3):
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super().__init__()
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self.main = nn.Sequential(
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# 매 input: nz × 1 × 1
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nn.ConvTranspose2d(nz, ngf*8, 4, 1, 0, bias=False),
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nn.BatchNorm2d(ngf*8),
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nn.ReLU(True),
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# 매 ngf*8 × 4 × 4
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nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False),
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nn.BatchNorm2d(ngf*4),
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nn.ReLU(True),
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# 매 ... → 매 nc × 64 × 64
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nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
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nn.Tanh(),
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)
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def forward(self, z):
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return self.main(z)
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class Discriminator(nn.Module):
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def __init__(self, ndf=64, nc=3):
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super().__init__()
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self.main = nn.Sequential(
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nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
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nn.LeakyReLU(0.2, True),
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nn.Conv2d(ndf, ndf*2, 4, 2, 1, bias=False),
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nn.BatchNorm2d(ndf*2),
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nn.LeakyReLU(0.2, True),
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# 매 ...
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nn.Conv2d(ndf*8, 1, 4, 1, 0, bias=False),
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nn.Sigmoid(),
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)
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def forward(self, x):
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return self.main(x).view(-1)
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```
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### Training loop
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```python
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G = Generator().to('cuda')
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D = Discriminator().to('cuda')
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opt_g = torch.optim.Adam(G.parameters(), lr=2e-4, betas=(0.5, 0.999))
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opt_d = torch.optim.Adam(D.parameters(), lr=2e-4, betas=(0.5, 0.999))
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criterion = nn.BCELoss()
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for real, _ in loader:
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real = real.to('cuda')
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bs = real.size(0)
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# 매 D step
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opt_d.zero_grad()
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d_real = D(real)
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loss_real = criterion(d_real, torch.ones(bs).to('cuda'))
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z = torch.randn(bs, 100, 1, 1).to('cuda')
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fake = G(z)
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d_fake = D(fake.detach())
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loss_fake = criterion(d_fake, torch.zeros(bs).to('cuda'))
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(loss_real + loss_fake).backward()
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opt_d.step()
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# 매 G step
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opt_g.zero_grad()
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d_fake = D(fake)
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loss_g = criterion(d_fake, torch.ones(bs).to('cuda'))
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loss_g.backward()
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opt_g.step()
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```
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### WGAN-GP (modern stable)
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```python
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def gradient_penalty(D, real, fake):
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bs = real.size(0)
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alpha = torch.rand(bs, 1, 1, 1).to(real.device)
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interp = alpha * real + (1 - alpha) * fake
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interp.requires_grad_()
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d_interp = D(interp)
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grads = torch.autograd.grad(d_interp.sum(), interp, create_graph=True)[0]
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grad_norm = grads.view(bs, -1).norm(2, dim=1)
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return ((grad_norm - 1) ** 2).mean()
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# 매 D step
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loss_d = D(fake).mean() - D(real).mean() + 10 * gradient_penalty(D, real, fake)
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```
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### Real-ESRGAN (super-resolution, modern application)
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```python
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from realesrgan import RealESRGAN
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from PIL import Image
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model = RealESRGAN(device='cuda', scale=4)
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model.load_weights('weights/RealESRGAN_x4.pth', download=True)
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img = Image.open('low_res.jpg')
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sr_img = model.predict(img)
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sr_img.save('high_res.jpg')
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```
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### CycleGAN (unpaired image-to-image)
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```python
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# 매 매 horse → 매 zebra (no pair)
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# 매 G_ab: 매 A → B, 매 G_ba: 매 B → A
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# 매 cycle loss: 매 G_ba(G_ab(a)) ≈ a
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```
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### StyleGAN inversion (modern)
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```python
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from stylegan2_pytorch import Trainer
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# 매 매 image 의 latent z 의 find
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def invert(image, generator, n_iters=1000):
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z = torch.randn(1, 512, requires_grad=True)
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for _ in range(n_iters):
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gen = generator(z)
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loss = (gen - image).pow(2).mean()
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loss.backward()
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z.data -= 0.01 * z.grad.data
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z.grad.zero_()
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return z
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```
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### FID evaluation
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```python
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from pytorch_fid import fid_score
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fid = fid_score.calculate_fid_given_paths(
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['./real_images/', './fake_images/'],
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batch_size=50,
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device='cuda',
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dims=2048,
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)
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print(f'FID: {fid:.2f}') # 매 lower 의 better
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```
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## 매 결정 기준
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| 응용 | Method |
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| Photoreal generation | Diffusion (SDXL, Flux) |
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| Face | StyleGAN3 |
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| Super-resolution | Real-ESRGAN |
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| Domain adapt (unpaired) | CycleGAN |
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| Sim2Real | CycleGAN / paired |
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| Fast inference | GAN > Diffusion |
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| Quality + control | Diffusion + ControlNet |
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**기본값**: 매 modern generation = Diffusion. 매 GAN = 매 SR / I2I 의 still.
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## 🔗 Graph
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- 부모: [[Generative-AI|Generative-Models]] · [[Deep Learning]]
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- 변형: [[Generative-Adversarial-Networks|GAN]] · [[StyleGAN]] · [[Pix2Pix]] · [[CycleGAN]]
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- 응용: [[Domain-Adaptation]] · [[CV_Synthesis]]
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- Adjacent: [[Diffusion-Models]] · [[Auto-Encoding]] · [[Stable-Diffusion]] · [[Deepfake-Technology]]
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## 🤖 LLM 활용
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**언제**: 매 GAN history. 매 fast generation. 매 super-res. 매 image-to-image.
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**언제 X**: 매 highest quality (use diffusion).
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## ❌ 안티패턴
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- **DCGAN 의 production 의 force**: 매 modern 의 diffusion 더 좋음.
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- **No mode collapse check**: 매 single output.
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- **WGAN-GP 없 의 unstable**: 매 training fail.
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- **FID 만 의 trust**: 매 다른 metric 도.
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## 🧪 검증 / 중복
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- Verified (Goodfellow GAN, Radford DCGAN, Karras StyleGAN, Real-ESRGAN).
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- 신뢰도 A.
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- Related: [[Diffusion-Models]] · [[Auto-Encoding]] · [[Stable-Diffusion]] · [[Deepfake-Technology]] · [[CV_Synthesis]] · [[Bioenergetics]] (model collapse).
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — DCGAN architecture + GAN evolution + 매 PyTorch / WGAN-GP / Real-ESRGAN code |
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