Arbitrary Style Transfer Using Graph Instance Normalization

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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 through normalizing the mean and variance of features. 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. Besides, this simple module is also applicable to other tasks like image-to-image translation or domain adaptation.
Publisher
IEEE Signal Processing Society
Issue Date
2020-10-26
Language
English
Citation

2020 IEEE International Conference on Image Processing (ICIP)

DOI
10.1109/ICIP40778.2020.9191195
URI
http://hdl.handle.net/10203/278682
Appears in Collection
EE-Conference Papers(학술회의논문)
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