To prevent generator collapse (mode collapse) in GANs when generating images from unstructured datasets, you can follow the following techniques:
- Mini-batch Discrimination: This helps the discriminator to consider the diversity of generated samples within a batch, which reduces the likelihood of the generator producing similar outputs.
- Feature Matching: The generator is encouraged to match the feature statistics of real data by using intermediate layer activations from the discriminator.
- Unsupervised Regularization: Regularization techniques such as adding noise to the inputs or using gradient penalties (e.g., in WGAN-GP) can help stabilize training.
- Multiple Generators: Using multiple generators to produce diverse outputs may reduce collapse.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- WGAN-GP: Uses the Wasserstein loss with a gradient penalty to stabilize training and prevent mode collapse.
- Gradient Penalty: Regularizes the discriminator, enforcing the 1-Lipschitz condition, which prevents unstable updates and mode collapse.
- Generator and Critic: The generator creates images from latent vectors, while the critic (discriminator) evaluates them, helping to improve image quality.
- Minimized Mode Collapse: The gradient penalty ensures that the discriminator's gradients are well-behaved and reduces the risk of the generator collapsing to a few modes of output.
Hence, by referring to above, you can prevent generator collapse when using GANs for image generation on unstructured datasets.
