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.