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
Citations
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Book ChapterDOI
Iterative Feature Transformation for Fast and Versatile Universal Style Transfer.
Tai-Yin Chiu,Danna Gurari +1 more
TL;DR: A new transformation that iteratively stylizes features with analytical gradient descent is proposed that can switch between artistic and photo-realistic style transfers and reduce distortion and artifacts.
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Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs
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Attentive Normalization for Conditional Image Generation
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Proceedings ArticleDOI
Aesthetic-Aware Image Style Transfer
TL;DR: A novel Aesthetic-Aware Model-Optimisation-Based Style Transfer (AAMOBST) model, which can decide colour and texture features separately and is able to keep one of them fixed while changing the other one, which is not applicable for previous methods.
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