How can you reduce variance in GAN-generated images using dropout techniques

Question

With the help of Python programming, can I know How to reduce variance in GAN-generated images using dropout techniques?

Answer 1

Dropout can help reduce variance in GAN-generated images by randomly deactivating neurons during training, which promotes generalization.

Here is the code snippet you can refer to:

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

• Dropout: Reduces overfitting by deactivating neurons randomly.
• Latent Input: Accepts random noise for image generation.
• LeakyReLU: Prevents dead neurons and improves gradient flow.
• Tanh Output: Scales output to [-1, 1] for pixel values.
• Customizable: Easily adaptable architecture for specific tasks.

Hence, by referring to above, you can reduce variance in GAN-generated images using dropout techniques.