Alright, i’ve done some tests, and results are as following:
on the same architecture as before, no changes using .eval before image sampling, though i kept using it since that is how i should’ve done it from the beginning
Also i’ve done three more tests: discriminator dropout 0.4, discriminator batchnorm, and both at the same time, here is how they look, results from:
Dropout 0.4 with no batchnorm 10 epochs:


Batchnorm with 0.4 dropout 36 epochs:


Batchnorm only 36 epochs:


I feel like batchnorm with no dropout works the best, even at 10th epoch:


But the problem of mode collapse still remains unsolved. No matter the input, generator creates the same pictures (up to pixel-by-pixel similarity). Any remedeis for that? I’ve heard that Unrolled GANs solve this problem, but maybe there is something else? Also, i can see the problem of big repetitive patterns. Is this something similar to checkerboard artifacts that transpose layers produce, just carried over from earlier layers?
Gif from batchnorm only run