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
Restore the Incomplete Calligraphy Based on Style Transfer
Mengxi Qin,Xin Chen +1 more
TL;DR: The experimental results show that the Densenet-pix2pix model based style transfer method can effectively restore the remaining calligraphy characters, and the generated Chinese characters are more delicate.
Book ChapterDOI
MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network
TL;DR: Zhang et al. as mentioned in this paper proposed Mask-Guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to guide the fine-grained image editing process.
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Neural Crossbreed: Neural Based Image Metamorphosis.
TL;DR: This is the first attempt to address image morphing using a pre-trained generative model in order to learn semantic transformation, and shows that Neural Crossbreed produces high quality morphed images, overcoming various limitations associated with conventional approaches.
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Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks
Sitao Xiang,Hao Li +1 more
TL;DR: This work proposes a metric learning-based method to explicitly encode the style of an artwork such that the style representation can be interpreted, manipulated and visualized through style-conditioned image generation through a Generative Adversarial Network.
Proceedings ArticleDOI
Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks
TL;DR: A data-driven adaptive normalization layer which is capable of learning the most appropriate normalization scheme that should be applied on the data and leads to significant performance improvements over several competitive normalization approaches, as demonstrated using a large-scale limit order book dataset.
References
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