Editing Gans (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ GAN vs. Diffusion Models vs. VAEs
! Property !! GAN !! Diffusion Model !! VAE
|-
| Sample quality || Very high (when stable) || State-of-the-art || Moderate (blurry)
|-
| Training stability || Poor (adversarial) || Stable || Stable
|-
| Mode coverage || Poor (mode collapse) || Excellent || Good
|-
| Sampling speed || Very fast (single forward pass) || Slow (many steps) || Fast
|-
| Latent space quality || Good (disentangled in StyleGAN) || Implicit || Explicit, smooth
|-
| Controllability || Moderate (cGAN) || High (guidance scale) || Moderate
|}

'''Failure modes:'''
* '''Mode collapse''' — G always generates the same output (e.g., always "8" for digit generation). Fix: mini-batch discrimination, Wasserstein loss, spectral normalization.
* '''Training oscillation''' — Loss curves oscillate wildly; G and D never converge. Fix: reduce learning rates, increase batch size, gradient penalty (WGAN-GP).
* '''Checkerboard artifacts''' — Upsampling with transposed convolutions creates grid-pattern artifacts. Fix: bilinear upsampling followed by standard convolution.
* '''Discriminator overfitting''' — D memorizes training data rather than learning a general real/fake boundary. Fix: discriminator dropout, data augmentation on real samples.
* '''Evaluation metric gaming''' — Optimizing FID specifically (rather than actual quality) can produce high FID-scoring but visually poor images. Use multiple metrics.
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