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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Visualizing Adapted Knowledge in Domain Transfer
Yunzhong Hou,Liang Zheng +1 more
TL;DR: This work proposes a source-free image translation method that generates source-style images using only target images and the two models to depict the adapted knowledge and finds that generated images successfully capture the style difference between the two domains.
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Style Normalization and Restitution for Domain Generalization and Adaptation
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Proceedings ArticleDOI
StandardGAN: Multi-Source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
TL;DR: In this article, the authors proposed a method called StandardGAN, which standardizes each source and target domains so that all the data have similar data distributions, and then use the standardized source domains to train a classifier and segment the standardized target domain.
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Microsoft COCO: Common Objects in Context
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