Closed Santana18 closed 6 years ago
In my experience, when the adv_loss comes to 0.5 (as the same time, dis_loss comes to 0), then i will stop training, since when the prediction network arrives at this circumstance, the network will be somewhat broken and the PSNR will be low. Next, i will decrease the learning rate of generator and discriminator by 0.1 times and then continue training.
Tested it on some new data out of interest. Is this a behavior that you saw during your training https://imgur.com/a/fiSc0iQ ? Did you implement any loss balancing strategies for the generator and the discriminator, e.g. only train one, if it isn't too much stronger than its adversary etc.?