Comments (1)
I haven't tried this implementation choice. Intuitively, I think this will have minor difference to the outcome. But maybe our implementation save more GPU consumption because the computational graph is maintained in the memory till calling the backward function again?
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Related Issues (18)
- Could you please provide the trained model? HOT 1
- No pattern expansion
- Excuse me, but do you have a larger train dataset? HOT 1
- Visualization of the intermediate result of residual blocks
- The usage of the style loss
- Pretrained models HOT 4
- Trained model
- vgg_conv.pth not available HOT 1
- Changes to work on pytorch 3.9.0 and cuda 11.3
- Quick Question HOT 1
- "trainA is not a valid directory" error
- backward_G in half_gan_style.py HOT 2
- CUDA error: out of memory HOT 3
- errors caused by network connection HOT 5
- Generalization ability of model HOT 4
- About experiments in your paper HOT 2
- Augmentation of diversity by shuffling tiles HOT 2
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