How would you handle generator mode collapse in a DCGAN when generating images from random noise

Question

Can you tell me How you would handle generator mode collapse in a DCGAN when generating images from random noise?

Answer 1

 You can handle generator mode collapse in a DCGAN when generating images from random noise, by referring to the following code:

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

• Mini-Batch Discrimination: Helps the discriminator detect diversity in a batch, encouraging the generator to produce varied outputs rather than collapse into a single mode.
• Feature Matching: This can also be used by forcing the generator to match intermediate features from the discriminator to improve diversity.
• Noise Injection: Adding noise to the inputs of the generator or discriminator can help prevent mode collapse by introducing randomness.

Hence, these techniques encourage the generator to explore the latent space more fully, avoiding the generation of identical images in different training steps.