WGAN-GP

Key Insight

The original GAN loss can stall: once the discriminator gets too good, it hands back almost no useful gradient, so the generator stops learning. Wasserstein GAN (WGAN) swaps that loss for the Earth Mover's Distance — the amount of "work" needed to reshape the pile of generated images into the pile of real ones — which gives a smooth, always-informative signal even when the two distributions barely overlap. For that distance to be valid the critic must be 1-Lipschitz (its output cannot change faster than its input), and this project enforces it with a gradient penalty: an extra loss term that pushes the size of the critic's input gradient toward 1, replacing the cruder weight-clipping of the original WGAN. The payoff you will see is far steadier training and much less fiddling with hyperparameters.