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
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Dissertation
Efficient, end-to-end and self-supervised methods for speech processing and generation
TL;DR: This thesis proposes the use of recent pseudo-recurrent structures, like self-attention models and quasi- Recurrent networks, to build acoustic models for text-to-speech and proposes a problem-agnostic speech encoder, named PASE, which is a fully convolutional network that yields compact representations from speech waveforms.
Posted Content
Pair-wise Exchangeable Feature Extraction for Arbitrary Style Transfer.
TL;DR: This paper argues that only aligning the global statistics of deep features cannot always guarantee a good style transfer and proposes to jointly analyze the input image pair and extract common/exchangeable style features between the two.
Posted Content
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network
Jialu Huang,Jing Liao,Sam Kwong +2 more
TL;DR: In this article, the authors proposed a new I2I translation method that generates a new model in the target domain via a series of model transformations on a pre-trained StyleGAN2 model.
Journal ArticleDOI
Neural arbitrary style transfer for portrait images using the attention mechanism
Sergey Berezin,Viktoriya Volkova +1 more
Posted Content
One-Shot Mutual Affine-Transfer for Photorealistic Stylization
Ying Qu,Zhenzhou Shao,Hairong Qi +2 more
TL;DR: The strong representative and discriminative power of the proposed network enables one-shot learning given only one content-style image pair, and is able to generate photorealistic photos without spatial distortion or abrupt color changes.
References
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Book ChapterDOI
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
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.