Hi, don’t know where else to ask but I just don’t know what else I could try out with my code.

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I’m trying to reimplement CycleGAN in a Jupyter notbook and (for me) the code looks good, but somehow my generators just learn to map an input to itself (so what I put into it comes out at the other end). I’m testing my implementation with the horse2zebra dataset.

First and third row: input, second and fourth row: output

Learning curves for one generator and one discriminator

What’s odd is that the GAN loss is going up, which is probably why the generators don’t learn anything meaningful other than the identity mapping. I also got the feeling that my discriminators just learn to distinguish fake from real images, but nothing about horses or zebras.

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Here’s a link to the notebook: https://github.com/kiwiwa/GANs-from-scratch/blob/master/cyclegan/cyclegan.ipynb

I would be so happy if somebody could give me a hint. The discriminator/generator architectures should be fine, probably the training process?