Comments (2)
We run HED [32] to convert input and output to edge images and then compute SSIM between them.
Gram loss is covariance difference between feature embeddings (VGG19) of input and output images. Please refer to [8] for more details.
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Were this resolved? There were some people that asked us the same code by email, and we gave them so that I am a little confused if this is the case.
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Related Issues (20)
- WCT2 seems cannot transfer styles without segmentation maps? Could you please indicate how to make it works without segmentation maps since you claimed in your paper that it supports transfer directly and without segmentation. HOT 1
- Can I train with my data? HOT 1
- Some questiona about network? HOT 4
- Query regarding "conv0" layer HOT 1
- Segmentation maps precision HOT 1
- In "cat5" skip network, only the last layer matters HOT 2
- Qs about the Dataset HOT 2
- Does it support large-size photos style transfer? HOT 1
- How to add temporal consistency HOT 2
- QS about the depth map of transfered images HOT 1
- About training source HOT 4
- why the encoder has the layer conv0 HOT 1
- How does it work for an indoor dataset? HOT 1
- Question about the loss functions
- LL components vs. avg. pooling HOT 2
- Details about training decoder
- About SSIM metircs
- Without Seg HOT 1
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