How do you apply dropout techniques for reducing overfitting in generative models like VAEs

Question

With the help of code can you tell me How do you apply dropout techniques for reducing overfitting in generative models like VAEs?

Answer 1

Apply Dropout in both the encoder and decoder of Variational Autoencoders (VAEs) to regularize latent space learning and prevent overfitting.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Dropout is applied in both Encoder & Decoder to prevent overfitting.

• Uses a reparameterization trick (Lambda(sampling)) for smooth latent space learning.

• Defines separate layers for mean and variance (z_mean, z_log_var) to create latent representations.

• Optimized with MSE loss and Adam optimizer for stable training.

Hence, applying Dropout in both encoder and decoder of VAEs effectively reduces overfitting, stabilizing latent space learning while preserving generative quality.