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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Proceedings ArticleDOI
MVStylizer: an efficient edge-assisted video photorealistic style transfer system for mobile phones
TL;DR: MVStylizer is proposed, an efficient edge-assisted photorealistic video style transfer system for mobile phones that can generate stylized videos with an even better visual quality compared to the state-of-the-art method while achieving 75.5× speedup for 1920×1080 videos.
Journal ArticleDOI
Style Permutation for Diversified Arbitrary Style Transfer
TL;DR: This article proposes a light-weighted yet efficient method named style permutation (SP) to tackle the limitation of the diversity without harming the original style information and shows that this method could generate diverse outputs for arbitrary styles when integrated into both WCT (whitening and coloring transform) and AdaIN (adaptive instance normalization)-based methods.
Proceedings ArticleDOI
Análise de Lesões de Pele usando Redes Generativas Adversariais
Alceu Bissoto,Sandra Avila +1 more
TL;DR: In this paper, the authors used GANs to generate synthetic data to augment the classification model's training dataset to boost the performance of skin lesion analysis, which is able to generate high-resolution clinically-meaningful skin lesions images, that when compound the training dataset, consistently improved the performance in different scenarios, for distinct datasets.
Posted Content
StyleRemix: An Interpretable Representation for Neural Image Style Transfer
TL;DR: This paper outlines a novel MST model, StyleRemix, to compactly and explicitly integrate multiple styles into one network by decomposing diverse styles into the same basis and represents a specific style in a continuous vector space with 1-dimensional coefficients.
Book
Computational Texture and Patterns: From Textons to Deep Learning
TL;DR: Visual pattern analysis is a fundamental tool in mining data for knowledge for knowledge and Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to summarize and store patterns.
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