Comments (1)
This would indeed make sense to me, but when I tried it I was unable to find an experimental setup where doing that improved the results in any consistent way. In my experiments it at least doesn't seem to hurt though, so if you want more theoretical elegance and don't mind the extra computation time you can probably do that and it'll still work.
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Related Issues (20)
- o._shape = TensorShape(new_shape) caused error in inception_score.py HOT 1
- Why the gradient penalty item decreases to zero and then grows to infinity ?
- This code is outdated seriously HOT 3
- inception_score.py: fixed the issue of ValueError "Cannot iterate over a shape with unknown rank"
- inception_score.py: ValueError in the method _init_inception() HOT 1
- Could it be possible to make the trained GAN publicly available?
- Mismatch between code and paper in the gradient penalty algorithm HOT 1
- Questions about the loss
- AttributeError: module '_pickle' has no attribute 'HIGHEST_PROTOCOL' HOT 1
- Error Conv2DCustomBackpropFilterOp only supports NHWC HOT 2
- Question of DEVICE in the gan_cifar10_resnet.py
- how to run itοΌ
- Critic loss curve
- a question about loss
- reproducing inception score on gan_cifar.py HOT 2
- If I intend to calculate gradient penalty for two dataset in differet dimension, what should I do?
- gan_mnist.py's ERROR HOT 1
- Query: WGAN-GP FID SCORE (PyTorch)
- Wire gide
- Conv2DCustomBackpropInputOp only supports NHWC
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