Open AccessProceedings Article
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.
Xun Huang,Serge Belongie +1 more
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TLDR
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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Image style transfer model with content preservation
TL;DR: A sub-structure named Important Content Contour Extraction (ICCE) is proposed to generate masks and therefore preserve the clear contour and content after style transfer and achieves a good compromise in speed, flexibility and quality.
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Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring.
TL;DR: In this article, the deblurring problem is divided into two successive tasks: (1) blur kernel estimation and (2) sharp image restoration, and a kernel-adaptive AE block encodes features from both blurred images and blur kernels into a low dimensional space and then decodes them simultaneously to obtain an appropriately synthesized feature representation.
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High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks.
TL;DR: It is postulated that the perception of image similarity is not an explicitly learned capability, but rather one that is a byproduct of learning others, and this claim is supported by leveraging representations learned from a diverse set of visual tasks and using them jointly to predict perceptual similarity.
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Disentangling the Spatial Structure and Style in Conditional VAE
TL;DR: The authors disentangle the latent space in cVAE into the spatial structure and style code, which are complementary to each other, with one of them being label relevant and the other $z_u$ irrelevant.
Journal ArticleDOI
A bi-directional facial attribute transfer framework: transfer your single facial attribute to a portrait illustration
TL;DR: A bi-directional facial attribute transfer method based on GAN (generative adversarial network) and latent representation in a new way that aims to transfer a target facial attribute with its basic shape from a reference photorealistic facial image to a source realistic portrait illustration and vice versa is proposed.
References
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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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