Training (DC)GAN, D(G(z)) goes to 0.5 while D(x) stays 0.9 and G(z) becomes corrupt
I'm currently training a DCGAN for 1x32x32 (channel, height, width) images.
Quite soon in training G(z) becomes reasonably realistic apart from a problem with the 'chessboard' artifacts being visible, but this should go away after lots of training?
However, after a long training session D(G(z)) goes to 0.5000 (and no longer changes) while D(x) stays between 0.8 and 0.9. Whenever D(G(z)) goes to 0.5 it also starts outputting fully black &amp; white images. Hence, the generator no longer produces anything that looks close to what's in the training dataset. G(z) just becomes a black or white square.

The network used is from the original DCGAN paper, adapter for 1x32x32 images. With relu already replaced to leaky relu.

Solved the problem by switching to WGAN-GP (https://arxiv.org/abs/1704.00028).
Turns out it is more stable while training.
