How would you apply instance-based learning in GANs to improve generated content quality

Question

With the help of a proper code explanation, can you tell me how you would apply instance-based learning in GANs to improve generated content quality?

Answer 1

Instance-based learning (IBL) in GANs can be applied by using a memory buffer to store high-quality examples of generated images and comparing new generations to those stored examples to improve content quality. This can be done by implementing a replay buffer for the generator.

Here's a short code example using a memory buffer for instance-based learning which you can refer:

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

• Replay Buffer: Stores high-quality generated images.
• Random Sampling: Select examples to compare and guide the generator.
• Similarity Comparison: Used to ensure generated images improve over time based on stored examples.

Hence, this approach helps the generator focus on producing content similar to high-quality instances, improving the output's overall quality.