# Tag Info

### What is the reason for mode collapse in GAN as opposed to WGAN?

I don't have a definite answer, but only a suspicion/idea: Looking at Figure 1 from the WGAN paper, we clear see that the JS divergence on the right is not continuous at $0$, hence not differentiable ...

### How to calculate the gradient penalty proposed in "Improved Training of Wasserstein GANs"?

First of all, the discriminator in WGAN does not give a value in the range $[0,1]$. Compared to the traditional discriminator, it has a linear activation in the output layer. Therefore, the authors ...
• 967
1 vote

### What to do with a GAN that trained well but got worse over time?

This is from my own experience with (Vanilla) GANs, so it might not translate exactly to your application, but maybe it gives some orientation. your learning rate seems quite high. I've quite ...
• 144
1 vote
Accepted

### Aren't scores in the Wasserstein GAN probabilities?

Figure 3 in the original WGAN paper is actually quite helpful to understand the difference between the score in WGAN and the probability in GAN (see screenshot below). The blue distribution are real ...
• 144
1 vote

### Do WGAN gradients require multi-variable calculus?

A function can be optimized, even if it has two inputs! First, note that the problem in question already occurs with traditional GANs as well. It might be easier to understand what is going on if you ...
• 2,620
1 vote

### Is Relativistic GAN better than WGAN-GP?

For anyone looking for answer, as explained in Paper: The relativistic discriminator: a key element missing from standard GAN!, Yes, Standard RaGAN with gradient penalty generate data of better ...
1 vote

### What is being optimized with WGAN loss? Is the generator maximizing or minimizing the critic value?

I think I understand what's happening with the loss functions now. Notation: D = discriminator/critic G = generator D(x) - Critic score on real data D(G(z)) - Critic score on fake data ∇_D - Critic ...
1 vote

### Why do we use a linear interpolation of fake and real data to penalize the gradient of discriminator in WGAN-GP

In [1], section 4, the authors mention the following: Sampling distribution We implicitly define $p_{\hat{x}}$ sampling uniformly along straight lines between pairs of points sampled from the data ...

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