To address non-diverse outputs in a GAN when generating multi-modal data, you can apply the following techniques:
- Use Conditional GANs (cGANs): Conditioning the GAN on additional information (e.g., class labels) helps the generator produce diverse outputs by guiding it to generate different modes for each condition.
- Introduce Latent Space Regularization: Encourage the generator to explore the latent space more effectively by adding regularization terms (e.g., uniformity loss or latent variable perturbation).
- Mini-Batch Discrimination: Prevents mode collapse by allowing the discriminator to check diversity across a batch of images instead of individual images.
- Use Multiple Generators: Train multiple generators to focus on different modes and combine their outputs.
- Progressive Growing: Gradually increase the resolution of images to allow the model to focus on fine details and produce more diverse outputs.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Conditional GAN: Conditioning the GAN on labels or other information helps generate diverse outputs for different conditions.
- Mini-Batch Discrimination: Helps improve diversity by making the discriminator sensitive to diversity across a batch.
- Latent Space Exploration: Encourage the model to explore the latent space fully by adding regularization.
Hence, these methods can effectively improve the diversity of outputs in multi-modal data generation tasks.
