How can I implement supervised contrastive loss in GANs for improved performance

Question

With the help of code, can you tell me how I can implement supervised contrastive loss in GANs for improved performance?

Answer 1

To implement Supervised Contrastive Loss in GANs for improved performance, you can integrate the loss into the discriminator by encouraging representations of the same class to be closer and different classes to be farther apart. 

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Features: Use the discriminator's intermediate layers to extract embeddings.
• Loss Integration: Combine contrastive loss with adversarial losses for training.
• Temperature Scaling: Adjust temperature for better separation.

Hence, this method enhances the semantic alignment of generated and real data representations.