Adversarial Generative Models
The minimax game, mode collapse, Wasserstein critics, and what FID does and does not measure.
7concepts
76flashcards
53minutes of reading
- 01 Adversarial Losses as a Component Why GANs largely lost as standalone generative models but their loss survived inside autoencoders, vocoders and super-resolution, and what a patch discriminator adds that L2 cannot.
- 02 Conditional GANs and Image-to-Image Translation How conditioning information enters a GAN's generator and discriminator, why pix2pix needs paired data while CycleGAN substitutes cycle consistency, what that substitute silently permits, and why the projection discriminator beat concatenation.
- 03 The GAN Minimax Game What the discriminator is actually estimating, why the theoretically clean generator loss cannot be used in practice, and what it means that training seeks an equilibrium rather than a minimum.
- 04 What FID Measures and What It Misses The Gaussian assumption inside Frechet Inception Distance, the sample-size bias that makes numbers incomparable across papers, and why precision and recall metrics exist.
- 05 Mode Collapse and Training Instability Why a GAN generator has no incentive to cover the data distribution, the difference between full and partial collapse, and what each of the standard mitigations actually changes.
- 06 StyleGAN and Style-Based Generation How StyleGAN moved the latent code out of the generator's input and into per-layer modulation through a learned mapping network, and how StyleGAN2 and StyleGAN3 traced visible artefacts back to normalisation, progressive growing and aliasing.
- 07 Wasserstein Critics and the Lipschitz Constraint Why earth mover distance still has a gradient when JS divergence does not, how the Kantorovich duality turns it into a trainable critic, and why enforcing the Lipschitz bound is where every practical difficulty lives.