<p>Solved the problem by switching to WGAN-GP (<a href="https://arxiv.org/abs/1704.00028" rel="nofollow noreferrer">https://arxiv.org/abs/1704.00028</a>).<br>
Turns out it is more stable while training.</p>
Training (DC)GAN, D(G(z)) goes to 0.5 while D(x) stays 0.9 and G(z) becomes corrupt
<p>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.</p>

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