Comments (2)
It sounds interesting. Maybe we could do some experiment on it. However, in my intuition, each layer of the decoder contains features of both content and style. That means there doesn't exist a layer only holds content features or style features. But I am sure that some layers hold content features with a higher weight, while the other layers hold "more" style features. So maybe we could use some strategy to split the content features and style features. After all, your idea is great and it is worth to do some research.
from arbitrary_style_transfer.
Thank you for your answer.
to be moer precise : This idea came from other implementation mainly The Leon A. Gatys implementions of style transfer.
In those implementations we use low level layers to compute the content loss and hight level layer and the gram matrix of the pastiche .
from arbitrary_style_transfer.
Related Issues (10)
- The results of this program which I got was not right. HOT 8
- Resource Exhausted Error while testing
- Converting to BGR in style_transfer_net.py HOT 1
- For README display
- Is it possible to run this on mobile device? HOT 1
- How should I train my own model? HOT 6
- VGG Model File Source (And a question) HOT 3
- when I train the model,why is it always interrupted? HOT 5
- yeah, I think your suggestion is right. And I will have a try. Thank you very much. And I still have the question: when you train the model, the loss is big as mine? The content loss is about 2000-5000 and it seems never converged.
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from arbitrary_style_transfer.