Comments (3)
lr is learning rate.
lambda_style and lambda_feat are the weighting factors for style and content respectively.
lambda_tv is the total variation factors.
I think you should read the paper " Perceptual Losses for Real-Time Style Transfer and Super-Resolution" firstly.
all of the parameters are explained in it.
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@bucktoothsir thanks! But after reading the material, i still don't understand how to emphasis the color of the original image?
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@bucktoothsir color is somewhat low level feature, if you only want to enchance the color, you can change the weight of style feature, maybe you should dive into the code.
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Related Issues (20)
- kanagawa
- the result is dark when it is used for super resolution
- Is there a style size?
- why do you set batch_size equal 1 HOT 1
- OutOfMemoryError when generate on Azure ND6 VM
- RuntimeError: CUDA environment is not correctly set up
- cupy.cuda.compiler.CompileException: nvrtc: error: failed to load builtins HOT 5
- LC_RPATH @executable_path error or libnvrtc-builtins.dylib error HOT 5
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- hello! can you code Fast Neural Style Transfer with Arbitrary Style ? HOT 6
- Memory error without GPU. HOT 1
- ValueError: test argument is not supported anymore. Use chainer.using_config HOT 5
- error in run train.py
- Style transfer between any two images - iOS App needs beta tester HOT 6
- volatile argument is not supported anymore. Use chainer.using_config HOT 10
- Output size few pixels smaller
- Artx - iOS App that transfer styles between any two images HOT 4
- how to download vgg16 and setup it ?
- error in sh setup_model.sh HOT 1
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