Update README.md

README.md

@@ -4,21 +4,13 @@ tags:
 - GAN
 ---
 
-## Model description
+## Wasserstein GAN (WGAN) with Gradient Penalty (GP)
 
-More information needed
+The original Wasserstein GAN leverages the Wasserstein distance to produce a value function that has better theoretical properties than the value function used in the original GAN paper. WGAN requires that the discriminator (aka the critic) lie within the space of 1-Lipschitz functions. The authors proposed the idea of weight clipping to achieve this constraint. Though weight clipping works, it can be a problematic way to enforce 1-Lipschitz constraint and can cause undesirable behavior, e.g. a very deep WGAN discriminator (critic) often fails to converge.
 
+The WGAN-GP method proposes an alternative to weight clipping to ensure smooth training. Instead of clipping the weights, the authors proposed a "gradient penalty" by adding a loss term that keeps the L2 norm of the discriminator gradients close to 1.
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
- ## Training Metrics
-Model history needed
- ## Model Plot
 
 <details>
 <summary>View Model Plot</summary>