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.

Related Post: How to handle overfitting in large generative models