Arbitrary Style Transfer using Graph Instance Normalization

Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content . In statistical methods, the adaptive instance normalization (AdaIN) whitens the source images and applies the style of target images . However, computing feature statistics for each instance would neglect the inherent relationship between features, so it is hard to learn global styles while fitting to the individual training dataset . In this paper, we present a novel learnable normalization technique for style transfer using graph convolutional networks, termed Graph Instance Normalization (GrIN) This algorithm makes the style transfer approach more robust by taking into account similar information shared between instances .

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Keywords : style - instance - transfer - normalization - graph -

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