I’ve set up a GAN that works for generating mnist digits, and I am now trying to get it working with cars from cifar10. However, the outputs for cars from cifar10 are usually monochrome and periodically switch to different colors as the generator loss decreases and the discriminator loss increases.
Here are the generator and discriminator loss graphs when training on mnist:
In contrast, the losses for cifar10 are flat for long periods before suddenly spiking up:
For cifar10 the GAN generates nearly monochrome images of varying shades for example:
After about 10 epochs it starts outputting interesting textures that don’t bear any resemblance to the images:
Letting it run for 50 epochs doesn’t improve image quality. The pattern in the loss becomes less apparent, but you can still see the regular spikes at the start of training:
I’ve tried messing with the learning rates, activation functions, number of layers, convolutional kernel size, noisy labels, flipped labels, noisy discriminator input and so forth, but nothing seems to work. It seems more likely that there is a larger problem than just incorrect hyperparameters.
Does anyone recognize this abnormal pattern in the loss?