I have a small and highly imbalanced low-resolution image dataset with 6 classes where 50% of the observations are from a single class. With unconditional GANs, I can get stably obtain samples with seemingly sufficient diversity under most setups (architecture, losses, etc).

​

In the conditional case, there is significant class leakage in the samples generated. I’ve tried various standards on class conditioning such as, (1) conditional batch norm in the generator; (2) a projective layer in the critic and; (3) increasing the batch size significantly to cover more modes in each batch. (1) and (2) seem to be helping, and (3) doesn’t appear to be helping with sample diversity and is making sample quality worse. Training is still in early stages though, so maybe things change (or modes collapse).

​

Are there any strategies or heuristics for conditional GANs specifically dealing with the class imbalanced case? At this point, I’m considering using balanced subsampled batches in each iteration or weighing the hinge loss by class distribution and hoping for the best.