How can I prevent mode collapse when training GANs on diverse datasets

Question

Can you tell me How can I prevent mode collapse when training GANs on diverse datasets?

Answer 1

To prevent mode collapse when training GANs on diverse datasets, use techniques such as adding noise to the inputs, implementing feature matching, or using Wasserstein GANs with gradient penalty. you can follow the following solutions:

• Wasserstein GAN: Use Wasserstein loss with gradient penalty to improve training stability.
• Feature Matching: Ensure the generator outputs diverse samples by matching feature statistics.
• Minibatch Discrimination: Allow the discriminator to compare different samples within a batch to detect mode collapse.

Here is the code snippet given below:

In the above code, we are using the following key points:

• Wasserstein GAN: Use Wasserstein loss with gradient penalty to stabilize training.
• Feature Matching: Match feature statistics between real and generated samples.
• Minibatch Discrimination: Prevent mode collapse by comparing multiple samples in each batch.