Created
April 16, 2017 07:19
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Histrogram style loss based on "Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses" (4.3)
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| # output -- per layer feature-map of the image we optimize | |
| # style -- per layer feature-map of the style image we want to copy | |
| # histogram_matching() -- see reference numpy implementation http://stackoverflow.com/a/33047048/7873678 | |
| output_remapped = histogram_matching(output, style, nbins=1024) # non-differentiable | |
| sum((output_image - output_remapped)**2) # gradients assume that output_remapped ~~ const |
Hi there, I see you wrote 'non-differentiable' as a comment there, so could this be used in back-propagation?
The line with that comment isn't differentiable, but the line below it is. So histogram loss (the two lines in combination) can be used in back propagation.
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where is the histogram calculation function ?