What strategies can I use to improve sample diversity in a generative model

Question

With the help of suitable Python programming, can you explain What strategies I can use to improve sample diversity in a generative model?

Answer 1

To improve sample diversity in a generative model, you can use strategies such as adding noise to the latent space and employing a more complex loss function.

Here is the code snippet below showing how it is done:

In the above code, we use the following key points:

• Noise in Latent Space: Adding noise in the reparameterization step (reparameterize function) helps improve diversity by allowing more variation in generated samples.
• KL Divergence: Regularize the latent space using KL Divergence to promote more diverse representations and avoid mode collapse.
• Loss Function: Use a combination of reconstruction loss and regularization to improve the diversity of generated samples.
• Data Augmentation: If using images, apply augmentation techniques (e.g., rotations, scaling) to introduce more variability into the training data.

Hence, by referring to the above, you can  improve sample diversity in a generative model