Arbitrary style transfer in real-time with adaptive instance normalization

Research output: Contribution to conference › Paper › Research › peer-review

Standard

Arbitrary style transfer in real-time with adaptive instance normalization. / Huang, Xun; Belongie, Serge.

2019. Paper presented at 5th International Conference on Learning Representations, ICLR 2017, Toulon, France.

Research output: Contribution to conference › Paper › Research › peer-review

Harvard

Huang, X & Belongie, S 2019, 'Arbitrary style transfer in real-time with adaptive instance normalization', Paper presented at 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24/04/2017 - 26/04/2017.

APA

Huang, X., & Belongie, S. (2019). Arbitrary style transfer in real-time with adaptive instance normalization. Paper presented at 5th International Conference on Learning Representations, ICLR 2017, Toulon, France.

Vancouver

Huang X, Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization. 2019. Paper presented at 5th International Conference on Learning Representations, ICLR 2017, Toulon, France.

Author

Huang, Xun ; Belongie, Serge. / Arbitrary style transfer in real-time with adaptive instance normalization. Paper presented at 5th International Conference on Learning Representations, ICLR 2017, Toulon, France.

Bibtex

@conference{3cea67f9af814c4a8b059578a4f3b996,
title = "Arbitrary style transfer in real-time with adaptive instance normalization",
abstract = "Gatys et al. (2015) 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.",
author = "Xun Huang and Serge Belongie",
note = "Publisher Copyright: {\textcopyright} 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. All Rights Reserved.; 5th International Conference on Learning Representations, ICLR 2017 ; Conference date: 24-04-2017 Through 26-04-2017",
year = "2019",
language = "English",

}

RIS

TY - CONF

T1 - Arbitrary style transfer in real-time with adaptive instance normalization

AU - Huang, Xun

AU - Belongie, Serge

N1 - Publisher Copyright: © 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. All Rights Reserved.

PY - 2019

Y1 - 2019

N2 - Gatys et al. (2015) 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.

AB - Gatys et al. (2015) 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.

UR - http://www.scopus.com/inward/record.url?scp=85083950854&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85083950854

T2 - 5th International Conference on Learning Representations, ICLR 2017

Y2 - 24 April 2017 through 26 April 2017

ER -

ID: 301823730