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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Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization
TL;DR: The proposed denoising method has great generalization ability, such that the network trained with synthetic-noise achieves the best performance for Darmstadt Noise Dataset (DND) among the methods from published papers.
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Arbitrary Video Style Transfer via Multi-Channel Correlation
TL;DR: Multi-Channel Correction network (MCCNet) is proposed, which can be trained to fuse the exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos.
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Neural Head Reenactment with Latent Pose Descriptors
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Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
TL;DR: An overview of GANs with a special focus on algorithms and applications for visual synthesis is provided and several important techniques to stabilize GAN training are covered, which has a reputation for being notoriously difficult.
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
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