What are effective ways to apply transfer learning to GANs for faster convergence on new datasets

Question

Can you name some effective ways to apply transfer learning to GANs for faster convergence on new datasets?

Answer 1

In order to effectively Apply Transfer Learning to GANs, here is the code you can refer to:

• Pretrained Generator or Discriminator: You can use weights from a pre-trained GAN on a similar dataset and fine-tune the target dataset.
• Freeze Layers Initially: You can freeze the lower layers of the generator or discriminator and train only the top layers to adapt quickly.
• Use Transfer of Latent Space Features: You can initialize the new dataset's latent space with embeddings learned from the pre-trained model.
• Progressive Growing: You can start training on lower resolutions and progressively increase to target resolution, leveraging pre-trained weights.

In the above code, we use pre-trained models to reduce training time and improve convergence, Freezing layers to preserve learned features from the source dataset, and Latent space initialization to provide a smooth transition for new data.

Hence, by using these techniques, you can effectively Apply Transfer Learning to GANs.