2nd/10_Wiki/Topics/AI_and_ML/Generative-Adversarial-Networks.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-generative-adversarial-networks	Generative Adversarial Networks (GAN)	10_Wiki/Topics	verified	self	GAN generative adversarial networks StyleGAN CycleGAN Goodfellow Wasserstein GAN	none	A	0.97	applied	deep-learning gan generative goodfellow stylegan image-generation		2026-05-10	pending	language framework Python PyTorch / TensorFlow

Generative Adversarial Networks (GAN)

매 한 줄

"매 generator 의 의 의 fake 의 의 의, discriminator 의 의 의 detect — 매 minimax game". Goodfellow 2014. 매 image gen 의 dominant (2014-2022) → 매 diffusion 의 의 의 displace. 매 modern: 매 StyleGAN3, 매 CycleGAN, 매 GAN-inversion.

매 핵심

매 model

• Generator G: 매 noise → fake.
• Discriminator D: 매 real or fake.
• Loss: G 의 의 fool D, D 의 의 catch.

매 famous variants

• DCGAN (Radford 2015): 매 conv-based.
• WGAN (Arjovsky 2017): 매 Wasserstein distance.
• WGAN-GP: 매 gradient penalty.
• StyleGAN v1/v2/v3 (Karras): 매 face quality.
• CycleGAN: 매 unpaired image translation.
• Pix2Pix: 매 paired translation.
• BigGAN: 매 class-conditional large.
• GAN inversion: 매 image → latent.

매 modern context (2024+)

• Diffusion dominate text-to-image.
• GAN niche: 매 fast inference, 매 specific style.
• GAN inversion for editing.
• StyleGAN still SOTA for faces.

매 응용

1. Image gen (faces).
2. Style transfer.
3. Super-resolution (ESRGAN).
4. Image-to-image (CycleGAN).
5. Data augmentation.
6. Anomaly detection (AnoGAN).

💻 패턴

DCGAN (PyTorch)

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, latent=100, img_dim=64):
        super().__init__()
        self.net = nn.Sequential(
            nn.ConvTranspose2d(latent, 512, 4, 1, 0), nn.BatchNorm2d(512), nn.ReLU(),
            nn.ConvTranspose2d(512, 256, 4, 2, 1), nn.BatchNorm2d(256), nn.ReLU(),
            nn.ConvTranspose2d(256, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.ReLU(),
            nn.ConvTranspose2d(128, 64, 4, 2, 1), nn.BatchNorm2d(64), nn.ReLU(),
            nn.ConvTranspose2d(64, 3, 4, 2, 1), nn.Tanh(),
        )
    
    def forward(self, z):
        return self.net(z.unsqueeze(-1).unsqueeze(-1))

class Discriminator(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv2d(3, 64, 4, 2, 1), nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2),
            nn.Conv2d(128, 256, 4, 2, 1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2),
            nn.Conv2d(256, 1, 4, 1, 0), nn.Sigmoid(),
        )
    
    def forward(self, x):
        return self.net(x).view(-1)

Train loop

G, D = Generator(), Discriminator()
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))
bce = nn.BCELoss()

for batch in dataloader:
    real = batch.cuda()
    bs = real.size(0)
    z = torch.randn(bs, 100).cuda()
    fake = G(z)
    
    # 매 D
    opt_D.zero_grad()
    d_real = D(real)
    d_fake = D(fake.detach())
    d_loss = bce(d_real, torch.ones(bs).cuda()) + bce(d_fake, torch.zeros(bs).cuda())
    d_loss.backward(); opt_D.step()
    
    # 매 G
    opt_G.zero_grad()
    d_fake_g = D(fake)
    g_loss = bce(d_fake_g, torch.ones(bs).cuda())
    g_loss.backward(); opt_G.step()

WGAN-GP loss

def gradient_penalty(D, real, fake, device):
    bs = real.size(0)
    alpha = torch.rand(bs, 1, 1, 1, device=device)
    interp = alpha * real + (1 - alpha) * fake
    interp.requires_grad_(True)
    d_interp = D(interp)
    grads = torch.autograd.grad(d_interp.sum(), interp, create_graph=True)[0]
    return ((grads.norm(2, dim=[1,2,3]) - 1) ** 2).mean()

# 매 D loss
d_loss = D(fake).mean() - D(real).mean() + 10 * gradient_penalty(D, real, fake, device)

CycleGAN (unpaired translation)

class CycleGAN:
    def __init__(self):
        self.G_AB, self.G_BA = Generator(), Generator()
        self.D_A, self.D_B = Discriminator(), Discriminator()
    
    def cycle_loss(self, real_A, real_B):
        fake_B = self.G_AB(real_A)
        rec_A = self.G_BA(fake_B)
        cycle_A = (rec_A - real_A).abs().mean()
        
        fake_A = self.G_BA(real_B)
        rec_B = self.G_AB(fake_A)
        cycle_B = (rec_B - real_B).abs().mean()
        
        return cycle_A + cycle_B

StyleGAN (style modulation)

class StyleBlock(nn.Module):
    def __init__(self, in_ch, out_ch, style_dim=512):
        super().__init__()
        self.conv = nn.Conv2d(in_ch, out_ch, 3, padding=1)
        self.style_proj = nn.Linear(style_dim, in_ch)
    
    def forward(self, x, style):
        scale = self.style_proj(style).unsqueeze(-1).unsqueeze(-1)
        x = x * scale
        return self.conv(x)

Spectral normalization

from torch.nn.utils import spectral_norm
class SNDiscriminator(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = spectral_norm(nn.Conv2d(3, 64, 4, 2, 1))
        # ...

Mode collapse detection

def diversity_check(generator, n=1000):
    z = torch.randn(n, 100).cuda()
    fake = generator(z)
    # 매 LPIPS pairwise
    distances = []
    for i in range(min(100, n)):
        for j in range(i+1, min(100, n)):
            distances.append(lpips(fake[i:i+1], fake[j:j+1]).item())
    return np.mean(distances)  # 매 low = mode collapse

FID (eval)

from torchmetrics.image.fid import FrechetInceptionDistance
fid = FrechetInceptionDistance().cuda()
fid.update(real_imgs, real=True)
fid.update(fake_imgs, real=False)
print(fid.compute())  # 매 lower = better

GAN inversion (project image → latent)

def gan_invert(target_image, G, n_iter=1000):
    z = torch.randn(1, 100, requires_grad=True, device='cuda')
    optim = torch.optim.Adam([z], lr=0.01)
    for _ in range(n_iter):
        gen = G(z)
        loss = ((gen - target_image) ** 2).mean() + lpips_loss(gen, target_image)
        optim.zero_grad(); loss.backward(); optim.step()
    return z

Conditional (class-conditional)

class CondG(nn.Module):
    def __init__(self, n_classes):
        super().__init__()
        self.embed = nn.Embedding(n_classes, 100)
        self.gen = Generator()
    
    def forward(self, z, label):
        return self.gen(z + self.embed(label))

ESRGAN (super-resolution)

# 매 conceptual
class RRDB(nn.Module):  # 매 residual-in-residual dense block
    pass

class ESRGAN(nn.Module):
    def __init__(self):
        super().__init__()
        self.body = nn.ModuleList([RRDB() for _ in range(23)])
        self.upsample = nn.Sequential(
            nn.Conv2d(64, 64*4, 3, padding=1), nn.PixelShuffle(2),
            nn.Conv2d(64, 3, 3, padding=1),
        )

매 결정 기준

상황	Approach
Modern image gen	Diffusion (not GAN)
Face generation	StyleGAN3
Unpaired translation	CycleGAN
Paired translation	Pix2Pix
Super-resolution	ESRGAN
Fast inference	GAN > Diffusion
Editing	GAN inversion

기본값: 매 modern = diffusion. 매 face = StyleGAN3. 매 niche translation = CycleGAN. 매 SR = ESRGAN. 매 always FID + diversity check.

🔗 Graph

• 부모: Deep Learning · Generative-AI
• 변형: StyleGAN · CycleGAN · Pix2Pix
• 응용: Data-Augmentation
• Adjacent: Diffusion-Models · VAE · Generative-AI

🤖 LLM 활용

언제: 매 fast image gen. 매 unpaired translation. 매 face. 언제 X: 매 text-to-image (use diffusion).

❌ 안티패턴

• Mode collapse 의 ignore: 매 limited diversity.
• No spectral norm: 매 unstable D.
• Imbalanced D-G: 매 collapse.
• No FID: 매 quality 의 invisible.
• GAN for text-to-image: 매 diffusion 이 better.

🧪 검증 / 중복

• Verified (Goodfellow 2014, Karras StyleGAN, Zhu CycleGAN).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-04-26	Auto
2026-05-08	Phase 1
2026-05-10	Manual cleanup — DCGAN/WGAN/Style/Cycle + 매 train / inversion / FID code