Comments (2)
In my view, there are two solutions. In this implementation, feature number N is equal to pixel number. However, the paper does not indicate strictly if feature should be a pixel. Thus a quick solution is 1) EntonyTang's interpretation, #1 (where the pitfall is you have only one feature. Not sure if this is correct. In this way, are attention maps as in the paper possible?) or another possible application is to 2) group a set of pixels as one feature, such as 10x10 pixels to get one attention map, to form less number of attention maps per layer.
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We have updated the 'sagan_models.py'! please check it out!
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Related Issues (20)
- About negative gamma HOT 10
- detach fake image when updating the discriminator
- model.py is working only for imsize=64 HOT 1
- Missing one 1x1 conv on output from attention layer? HOT 4
- UnboundLocalError: local variable 'dataset' referenced before assignment HOT 4
- dropbox link missing HOT 1
- The code is different from the original paper HOT 2
- RuntimeError: cublas runtime error : the GPU program failed to execute at /pytorch/aten/src/THC/THCBlas.cu:450
- 1
- the meaning of Gamma in Attention model HOT 2
- How to make the repo available for input image of 256x256 size? HOT 1
- torch.bmm(), CUDA out of memory. HOT 3
- Negative self.gamma parameter??
- Confused by self-attention layer positioning in Discriminator HOT 1
- self.gamma*out considered as "in place" operation
- Trying the Self-Attention-GAN with dog images
- Add examples to work with audio files as well
- 我长期研究和改进GAN,如果对GAN或者深度学习感兴趣的可以联系我,联系方式,wechat: lovedaixiaobaby
- How can I determine if the model has converged?
- the download.sh can't use
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