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This is a repository of image stylization, include conditional instance normalization,conditional weight normalization,conditional stylized-params normalization,adaptive instance normalization and perceptural losses

Robinatp/Tensorflow_image_stylization

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Tensorflow_image_stylization

This is a repository of image stylization by Tensorflow implementation , include conditional instance normalization,conditional weight normalization, conditional stylized-params normalization,adaptive instance normalization and perceptural losses

Reference

1,perceptural losses:fast-neural-style-tensorflow

Perceptual Losses for Real-Time Style Transfer and Super-Resolution, which first proposes an image transformation network to transform input image into a pastiche outpur image and a loss network pretrained for image classification to define perceptual loss function

2,conditional instance normalization,conditional weight normalization:image_stylization

A Learned Representation for Artistic Style, which proposes conditional instance normalization,conditional weight normalization to get multiple styles per module and combile the arbitrary styles by weight

3,conditional stylized-params normalization:arbitrary_image_stylization

Exploring the structure of a real-time, arbitrary neural artistic stylization network, which proposes a new style prediction network that directly predicts an embedding vector S from an input style image for the style transfer network

4,adaptive instance normalization:Tensorflow-Style-Transfer-with-Adain

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization, which propoces an AdaIn layer using to perform style transfer in the feature sapce

All of method above have a fixed weight of a loss network pretrained for image classification in the train session,however in the inference there only a style transfer network which build a encoder-decoder network except arbitrary_image_stylization which specially include style prediction network additionaly

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This is a repository of image stylization, include conditional instance normalization,conditional weight normalization,conditional stylized-params normalization,adaptive instance normalization and perceptural losses

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