Open AccessProceedings Article
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.
Xun Huang,Serge Belongie +1 more
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In this article, adaptive instance normalization (AdaIN) is proposed to align the mean and variance of the content features with those of the style features, which enables arbitrary style transfer in real-time.Abstract:
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.read more
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Frustratingly Simple Domain Generalization via Image Stylization.
TL;DR: This work addresses the Domain Generalization problem, where the classifier must generalize to an unknown target domain, and demonstrates an extremely simple yet effective method, namely correcting this bias by augmenting the dataset with stylized images.
Book ChapterDOI
CONFIG: Controllable Neural Face Image Generation
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Journal ArticleDOI
Contactless Palmprint Identification Using Deeply Learned Residual Features
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References
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