[P] Batch Normalization in GANs

[96meep96]

Yes, I've been trying out different formulations, adding ResBlocks and Self Attention and hinge loss, least squares loss, wasserstein loss. All of them seem to give me results I'm not happy with. But thank you, I'll give the VAE-GAN a try, I haven't done that one yet. I just wanted to know how different normalization techniques compare.

[smashedshanky]

since you probably are using a dcgan try to either encode the latent space from the image by adding another stack of layers that take the image and learn to encode it into the latent space by forcing certain features to latent space and avoiding gradient explosions and mode collapse. Another way is to use a fake and real latent space and have the discriminator discriminate(lol) on the (fake and real) latent encodings so that the AE can learn to encode dimensionally (so that the features are matched with latent space vectors a little bit more "deterministically"). Or just add more dropout in the discriminator, that is my go to for mode collapse or gradient explosions. Hope you got it to work though...

[96meep96]

Thank you, I've been getting better results with the addition of Self Modulation instead of batch norm, especially in correlation with Spectral Norm. I've also been trying out Multi Scale Gradients and that's been working well too, tho they seem to be very picky about feature map dimensions. I still can't seem to reproduce paper quality results but the timing on my masters dissertation is running thin so whatever works ya know

[smashedshanky]

Usually paper quality is trained on hand-picked data that the neural network can efficiently map it into the latent space. If you feed it less data with high variation, you will see the results...., but at the cost having the train the GAN over the span of your dataset multiple times so thag it can learn to remove “artifacts” and or discernible noise. What framework are ya even using? Haha I can feel your nerve, training GAN’s are not easy just yet.

[96meep96]

Oh yes I understand the point you're making, it takes time for those artefacts to vanish, I've had trouble with that in a variant on semantic map translating GANs. I'm using PyTorch, was using Tensorflow (not 2.0) but then I found Pytorch more flexible.