How do I keep GAN from falling mode?

How do I keep GAN from falling mode?

A carefully tunned learning rate may mitigate some serious GAN’s problems like mode collapse. In specific, lower the learning rate and redo the training when mode collapse happens. We can also experiment with different learning rates for the generator and the discriminator.

Why does mode collapse happen in GAN?

Mode collapse happens when the generator can only produce a single type of output or a small set of outputs. This may happen due to problems in training, such as the generator finds a type of data that is easily able to fool the discriminator and thus keeps generating that one type.

Which is loss function does TF-Gan use?

In TF-GAN, see modified_generator_loss for an implementation of this modification. By default, TF-GAN uses Wasserstein loss. This loss function depends on a modification of the GAN scheme (called “Wasserstein GAN” or “WGAN”) in which the discriminator does not actually classify instances.

How is the Gan training algorithm and loss function trained?

In the first case, the generator is trained to minimize the probability of the discriminator being correct. With this change to the loss function, the generator is trained to maximize the probability of the discriminator being incorrect. In the minimax game, the generator minimizes the log-probability of the discriminator being correct.

Is the minimax Gan loss the same as the saturating Gan loss?

The approach was introduced with two loss functions: the first that has become known as the Minimax GAN Loss and the second that has become known as the Non-Saturating GAN Loss. Under both schemes, the discriminator loss is the same.

What is non-saturating Gan loss in generative adversarial network?

The Non-Saturating GAN Loss is a modification to the generator loss to overcome the saturation problem. It is a subtle change that involves the generator maximizing the log of the discriminator probabilities for generated images instead of minimizing the log of the inverted discriminator probabilities for generated images.