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
Multi-style Generative Network for Real-Time Transfer
Hang Zhang,Kristin J. Dana +1 more
TL;DR: MSG-Net is the first to achieve real-time brush-size control in a purely feed-forward manner for style transfer and is compatible with most existing techniques including content-style interpolation, color-preserving, spatial control and brush stroke size control.
Journal ArticleDOI
Knowledge Transfer for Rotary Machine Fault Diagnosis
TL;DR: An overview on recent development of knowledge transfer for rotary machine fault diagnosis (RMFD) by using different transfer learning techniques and research trends on transfer learning in the field of RMFD are discussed.
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TextureGAN: Controlling Deep Image Synthesis with Texture Patches
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Proceedings ArticleDOI
Infrared and Visible Image Fusion using a Deep Learning Framework
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TL;DR: Li et al. as discussed by the authors proposed an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images, which achieved state-of-the-art performance in both objective assessment and visual quality.
Proceedings ArticleDOI
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks
TL;DR: A Style-based Recalibration Module (SRM), a simple yet effective architectural unit, which adaptively recalibrates intermediate feature maps by exploiting their styles and effectively enhances the representational ability of a CNN.
References
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