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View Code? Open in Web Editor NEWOne-Stage GAN for Efficient Adversarial Learning. The implementation of CVPR 2021 paper: Training Generative Adversarial Networks in One Stage.
One-Stage GAN for Efficient Adversarial Learning. The implementation of CVPR 2021 paper: Training Generative Adversarial Networks in One Stage.
OSGAN/train_dcgan_symmetric_one_stage.py
Line 53 in fdfea7e
Does this make gradient direction of G and D the same ?
I changed the adversarial loss to hinge gan loss, and gamma will often be -inf in the training loop, then the training will be crash.
How can I adjust the "get_gradient_ratios" to prevent gamma from being -inf?
Thanks for sharing your code and it's really a great work.
Can your technique be applied to PatchGAN?
Thanks for sharing your code and it's really a great work.
I would like to apply this technique to other models using GANs to reduce the training time.
For implementation, I have two questions about your code.
reduction='none'
and get the average from loss_fake
in one stage ?Thank you.
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