Antigravity Agent
f8b21af4be
10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
256 lines
7.6 KiB
Markdown
256 lines
7.6 KiB
Markdown
---
|
||
id: wiki-2026-0508-dcgan
|
||
title: DCGAN (Deep Convolutional GAN)
|
||
category: 10_Wiki/Topics
|
||
status: verified
|
||
canonical_id: self
|
||
aliases: [DCGAN, GAN, generative adversarial network, StyleGAN, CycleGAN, Pix2Pix]
|
||
duplicate_of: none
|
||
source_trust_level: A
|
||
confidence_score: 0.88
|
||
verification_status: applied
|
||
tags: [gan, dcgan, generative-models, deep-learning, image-generation, stylegan, cyclegan, history]
|
||
raw_sources: []
|
||
last_reinforced: 2026-05-10
|
||
github_commit: pending
|
||
tech_stack:
|
||
language: Python
|
||
framework: PyTorch
|
||
---
|
||
|
||
# DCGAN
|
||
|
||
## 매 한 줄
|
||
> **"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 매 modern: 매 Diffusion 의 superseded 가, 매 fast inference / GAN-based super-res / image-to-image 의 still relevant.
|
||
|
||
## 매 핵심
|
||
|
||
### 매 GAN basics (Goodfellow 2014)
|
||
- **Generator**: 매 noise → 매 image.
|
||
- **Discriminator**: 매 real vs fake.
|
||
- **Min-max game**.
|
||
|
||
### DCGAN (2015) 의 contribution
|
||
1. **Strided conv** (no pooling).
|
||
2. **BatchNorm** in both G and D.
|
||
3. **No fully-connected hidden** layer.
|
||
4. **ReLU in G** (Tanh output).
|
||
5. **LeakyReLU in D**.
|
||
|
||
### 매 famous GAN evolution
|
||
- **DCGAN** (2015): 매 stable.
|
||
- **WGAN** (2017): 매 Wasserstein loss, 매 mode collapse 의 mitigate.
|
||
- **Pix2Pix** (2017): 매 image-to-image.
|
||
- **CycleGAN** (2017): 매 unpaired.
|
||
- **StyleGAN** (2018-2021): 매 face quality SOTA.
|
||
- **BigGAN** (2018): 매 large-scale.
|
||
- **GigaGAN** (2023): 매 text-to-image GAN.
|
||
|
||
### 매 mode collapse
|
||
- 매 G 의 매 limited variety 의 generate.
|
||
- 매 mitigation: 매 minibatch discrimination, 매 spectral norm, 매 WGAN.
|
||
|
||
### 매 evaluation
|
||
- **FID** (Fréchet Inception Distance): 매 generated vs real 의 distance.
|
||
- **IS** (Inception Score).
|
||
- **Precision / Recall** (Kynkäänniemi 2019).
|
||
|
||
### 매 modern relevance
|
||
- **Super-resolution** (ESRGAN, Real-ESRGAN).
|
||
- **Image-to-image** (CycleGAN 의 still useful).
|
||
- **Domain adaptation** (sim2real).
|
||
- **Implicit EBM**.
|
||
- **Fast generation** (vs slow diffusion).
|
||
|
||
### 매 vs Diffusion
|
||
| 측면 | GAN | Diffusion |
|
||
|---|---|---|
|
||
| Quality | High (StyleGAN) | Highest |
|
||
| Diversity | Mode collapse risk | High |
|
||
| Training stability | Tricky | Stable |
|
||
| Inference speed | Fast (single step) | Slow (multi-step) |
|
||
| Conditioning | Hard | Easy (CLIP) |
|
||
|
||
## 💻 패턴
|
||
|
||
### DCGAN (PyTorch)
|
||
```python
|
||
import torch.nn as nn
|
||
|
||
class Generator(nn.Module):
|
||
def __init__(self, nz=100, ngf=64, nc=3):
|
||
super().__init__()
|
||
self.main = nn.Sequential(
|
||
# 매 input: nz × 1 × 1
|
||
nn.ConvTranspose2d(nz, ngf*8, 4, 1, 0, bias=False),
|
||
nn.BatchNorm2d(ngf*8),
|
||
nn.ReLU(True),
|
||
# 매 ngf*8 × 4 × 4
|
||
nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False),
|
||
nn.BatchNorm2d(ngf*4),
|
||
nn.ReLU(True),
|
||
# 매 ... → 매 nc × 64 × 64
|
||
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
|
||
nn.Tanh(),
|
||
)
|
||
|
||
def forward(self, z):
|
||
return self.main(z)
|
||
|
||
class Discriminator(nn.Module):
|
||
def __init__(self, ndf=64, nc=3):
|
||
super().__init__()
|
||
self.main = nn.Sequential(
|
||
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
|
||
nn.LeakyReLU(0.2, True),
|
||
nn.Conv2d(ndf, ndf*2, 4, 2, 1, bias=False),
|
||
nn.BatchNorm2d(ndf*2),
|
||
nn.LeakyReLU(0.2, True),
|
||
# 매 ...
|
||
nn.Conv2d(ndf*8, 1, 4, 1, 0, bias=False),
|
||
nn.Sigmoid(),
|
||
)
|
||
|
||
def forward(self, x):
|
||
return self.main(x).view(-1)
|
||
```
|
||
|
||
### Training loop
|
||
```python
|
||
G = Generator().to('cuda')
|
||
D = Discriminator().to('cuda')
|
||
opt_g = torch.optim.Adam(G.parameters(), lr=2e-4, betas=(0.5, 0.999))
|
||
opt_d = torch.optim.Adam(D.parameters(), lr=2e-4, betas=(0.5, 0.999))
|
||
criterion = nn.BCELoss()
|
||
|
||
for real, _ in loader:
|
||
real = real.to('cuda')
|
||
bs = real.size(0)
|
||
|
||
# 매 D step
|
||
opt_d.zero_grad()
|
||
d_real = D(real)
|
||
loss_real = criterion(d_real, torch.ones(bs).to('cuda'))
|
||
|
||
z = torch.randn(bs, 100, 1, 1).to('cuda')
|
||
fake = G(z)
|
||
d_fake = D(fake.detach())
|
||
loss_fake = criterion(d_fake, torch.zeros(bs).to('cuda'))
|
||
|
||
(loss_real + loss_fake).backward()
|
||
opt_d.step()
|
||
|
||
# 매 G step
|
||
opt_g.zero_grad()
|
||
d_fake = D(fake)
|
||
loss_g = criterion(d_fake, torch.ones(bs).to('cuda'))
|
||
loss_g.backward()
|
||
opt_g.step()
|
||
```
|
||
|
||
### WGAN-GP (modern stable)
|
||
```python
|
||
def gradient_penalty(D, real, fake):
|
||
bs = real.size(0)
|
||
alpha = torch.rand(bs, 1, 1, 1).to(real.device)
|
||
interp = alpha * real + (1 - alpha) * fake
|
||
interp.requires_grad_()
|
||
|
||
d_interp = D(interp)
|
||
grads = torch.autograd.grad(d_interp.sum(), interp, create_graph=True)[0]
|
||
grad_norm = grads.view(bs, -1).norm(2, dim=1)
|
||
return ((grad_norm - 1) ** 2).mean()
|
||
|
||
# 매 D step
|
||
loss_d = D(fake).mean() - D(real).mean() + 10 * gradient_penalty(D, real, fake)
|
||
```
|
||
|
||
### Real-ESRGAN (super-resolution, modern application)
|
||
```python
|
||
from realesrgan import RealESRGAN
|
||
from PIL import Image
|
||
|
||
model = RealESRGAN(device='cuda', scale=4)
|
||
model.load_weights('weights/RealESRGAN_x4.pth', download=True)
|
||
|
||
img = Image.open('low_res.jpg')
|
||
sr_img = model.predict(img)
|
||
sr_img.save('high_res.jpg')
|
||
```
|
||
|
||
### CycleGAN (unpaired image-to-image)
|
||
```python
|
||
# 매 매 horse → 매 zebra (no pair)
|
||
# 매 G_ab: 매 A → B, 매 G_ba: 매 B → A
|
||
# 매 cycle loss: 매 G_ba(G_ab(a)) ≈ a
|
||
```
|
||
|
||
### StyleGAN inversion (modern)
|
||
```python
|
||
from stylegan2_pytorch import Trainer
|
||
|
||
# 매 매 image 의 latent z 의 find
|
||
def invert(image, generator, n_iters=1000):
|
||
z = torch.randn(1, 512, requires_grad=True)
|
||
for _ in range(n_iters):
|
||
gen = generator(z)
|
||
loss = (gen - image).pow(2).mean()
|
||
loss.backward()
|
||
z.data -= 0.01 * z.grad.data
|
||
z.grad.zero_()
|
||
return z
|
||
```
|
||
|
||
### FID evaluation
|
||
```python
|
||
from pytorch_fid import fid_score
|
||
|
||
fid = fid_score.calculate_fid_given_paths(
|
||
['./real_images/', './fake_images/'],
|
||
batch_size=50,
|
||
device='cuda',
|
||
dims=2048,
|
||
)
|
||
print(f'FID: {fid:.2f}') # 매 lower 의 better
|
||
```
|
||
|
||
## 매 결정 기준
|
||
| 응용 | Method |
|
||
|---|---|
|
||
| Photoreal generation | Diffusion (SDXL, Flux) |
|
||
| Face | StyleGAN3 |
|
||
| Super-resolution | Real-ESRGAN |
|
||
| Domain adapt (unpaired) | CycleGAN |
|
||
| Sim2Real | CycleGAN / paired |
|
||
| Fast inference | GAN > Diffusion |
|
||
| Quality + control | Diffusion + ControlNet |
|
||
|
||
**기본값**: 매 modern generation = Diffusion. 매 GAN = 매 SR / I2I 의 still.
|
||
|
||
## 🔗 Graph
|
||
- 부모: [[Generative-AI|Generative-Models]] · [[Deep-Learning]]
|
||
- 변형: [[Generative-Adversarial-Networks|GAN]] · [[StyleGAN]] · [[Pix2Pix]] · [[CycleGAN]]
|
||
- 응용: [[Domain-Adaptation]] · [[CV_Synthesis]]
|
||
- Adjacent: [[Diffusion-Models]] · [[Auto-Encoding]] · [[Stable-Diffusion]] · [[Deepfake-Technology]]
|
||
|
||
## 🤖 LLM 활용
|
||
**언제**: 매 GAN history. 매 fast generation. 매 super-res. 매 image-to-image.
|
||
**언제 X**: 매 highest quality (use diffusion).
|
||
|
||
## ❌ 안티패턴
|
||
- **DCGAN 의 production 의 force**: 매 modern 의 diffusion 더 좋음.
|
||
- **No mode collapse check**: 매 single output.
|
||
- **WGAN-GP 없 의 unstable**: 매 training fail.
|
||
- **FID 만 의 trust**: 매 다른 metric 도.
|
||
|
||
## 🧪 검증 / 중복
|
||
- Verified (Goodfellow GAN, Radford DCGAN, Karras StyleGAN, Real-ESRGAN).
|
||
- 신뢰도 A.
|
||
- Related: [[Diffusion-Models]] · [[Auto-Encoding]] · [[Stable-Diffusion]] · [[Deepfake-Technology]] · [[CV_Synthesis]] · [[Bioenergetics]] (model collapse).
|
||
|
||
## 🕓 Changelog
|
||
| 날짜 | 변경 |
|
||
|---|---|
|
||
| 2026-05-08 | Phase 1 |
|
||
| 2026-05-10 | Manual cleanup — DCGAN architecture + GAN evolution + 매 PyTorch / WGAN-GP / Real-ESRGAN code |
|