How do you deal with vanishing or exploding gradients when training deep generative models GANs VAEs

Question

Can you tell me in 4 points how exploding gradient can be handled when training deep generative models like GANs and VAEs?

Answer 1

You can deal with vanishing or exploding gradients when training deep generative models GANs, VAEs by referring to the steps below:

• Gradient Clipping: Cap gradients to a maximum norm.

• Proper Weight Initialization: Use initialization techniques like Xavier or He initialization.

• Batch Normalization: Normalize layer inputs to stabilize training.

• Adaptive Optimizers: Use optimizers like Adam or RMSprop to handle gradient scaling.

• Smaller Learning Rates: Reduce the learning rate to control gradient updates.

• Spectral Normalization (GANs): Normalize weights to constrain Lipschitz constant.

• Skip Connections (VAEs): Use residual connections to mitigate gradient issues.

Here are the code references for the above steps which you can refer:

Hence, referring to the above, you deal with vanishing or exploding gradients when training deep generative models GANs VAEs.