Forgot to say in my previous message, but thanks for the help and advice! Really appreciate it.
Yes, it is only one positive class per sample. My batch size is 128. I could weight the losses, but how would I go about doing so such that I don’t severely compromise the two objectives (real vs fake and category/class distinction)? The class distinction is pretty important, but obviously I wouldn’t want the discriminator to be terrible at recognising fakes.
Any weighting advice? Or just trial-and-error weighting parameters (that could maybe change during training if a loss threshold is achieved)?
Thanks again!