vandit15 / self-supervised-gans-pytorch Goto Github PK
View Code? Open in Web Editor NEWReady to train Pytorch implementation of the CVPR'19 paper "Self-Supervised GANs via Auxiliary Rotation Loss"
License: MIT License
Ready to train Pytorch implementation of the CVPR'19 paper "Self-Supervised GANs via Auxiliary Rotation Loss"
License: MIT License
Hello, I have a question. I found in your code that you use WGAN loss function, which is different from the loss function used in the original paper, and you use torch.sum()
operation instead of torch.mean()
operation to calculate the loss for a batch of samples, may I ask why you do so
In the paper, only upright images are used to compute real/fake logits, but it seems like in your code, images are packed with their rotations to feed into the discriminator, and I wonder where the rotated images are excluded from computing the real/fake logits?
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