How do you resolve training instability in cycle-consistent GANs during image translation tasks

Question

Can you tell me how you resolve training instability in cycle-consistent GANs during image translation tasks?

Answer 1

Training instability in CycleGANs can be mitigated by using techniques such as:

• Gradient Penalty: Ensures smoother gradients for the discriminator.
• Learning Rate Scheduling: Adjusts the learning rate dynamically to stabilize training.
• Label Smoothing: Prevents overconfidence in discriminator predictions, reducing instability.
• Better Initialization: Ensures initial weights are set properly to avoid unstable training.

Here is an example of using gradient penalty and learning rate scheduling:

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

• Gradient Penalty: Stabilizes training by penalizing large gradients, preventing mode collapse.
• Learning Rate Scheduling: Gradually decreases the learning rate after a certain number of epochs to prevent overshooting.
• Label Smoothing & Initialization: This can be added similarly to improve training stability.

Hence, these techniques help prevent the common issues of unstable training and mode collapse in CycleGANs.