How can I prevent overfitting in a generative model using a dropout layer

Question

With the help of proper code and snippets, can you tell me How I can prevent overfitting in a generative model using a dropout layer?

Answer 1

To prevent overfitting in a generative model, you can incorporate a Dropout layer into the generator or discriminator. Dropout randomly zeroes out a fraction of activations during training, promoting generalization.

Here is the code snippet you can refer to:

In the above code, we are using the following. 

• Where to Apply Dropout: Add it in dense layers or convolutional layers in both generator and discriminator.
• Dropout Rate: Use a moderate rate (e.g., 0.2–0.5) to balance regularization without excessive information loss.
• Training Behavior: Dropout is active during training and disabled during inference.

Hence, this technique reduces overfitting by forcing the model to rely on different subsets of activations during each update, improving generalization.