Autoencoding beyond pixels using a learned similarity metric

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Autoencoding beyond pixels using a learned similarity metric. / Larsen, Anders Boesen Lindbo; Sønderby, Søren Kaae; Larochelle, Hugo; Winther, Ole.

Proceedings of The 33rd International Conference on Machine Learning. ed. / Maria Florina Balcan; Kilian Q. Weinberger. 2016. p. 1558–1566 (JMLR: Workshop and Conference Proceedings, Vol. 48).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Larsen, ABL, Sønderby, SK, Larochelle, H & Winther, O 2016, Autoencoding beyond pixels using a learned similarity metric. in MF Balcan & KQ Weinberger (eds), Proceedings of The 33rd International Conference on Machine Learning. JMLR: Workshop and Conference Proceedings, vol. 48, pp. 1558–1566, 33rd International Conference on Machine Learning, New York, United States, 19/06/2016. <http://www.jmlr.org/proceedings/papers/v48/larsen16.html>

APA

Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016). Autoencoding beyond pixels using a learned similarity metric. In M. F. Balcan, & K. Q. Weinberger (Eds.), Proceedings of The 33rd International Conference on Machine Learning (pp. 1558–1566). JMLR: Workshop and Conference Proceedings Vol. 48 http://www.jmlr.org/proceedings/papers/v48/larsen16.html

Vancouver

Larsen ABL, Sønderby SK, Larochelle H, Winther O. Autoencoding beyond pixels using a learned similarity metric. In Balcan MF, Weinberger KQ, editors, Proceedings of The 33rd International Conference on Machine Learning. 2016. p. 1558–1566. (JMLR: Workshop and Conference Proceedings, Vol. 48).

Author

Larsen, Anders Boesen Lindbo ; Sønderby, Søren Kaae ; Larochelle, Hugo ; Winther, Ole. / Autoencoding beyond pixels using a learned similarity metric. Proceedings of The 33rd International Conference on Machine Learning. editor / Maria Florina Balcan ; Kilian Q. Weinberger. 2016. pp. 1558–1566 (JMLR: Workshop and Conference Proceedings, Vol. 48).

Bibtex

@inproceedings{bacf45f293f24c06989d89fa70a84267,
title = "Autoencoding beyond pixels using a learned similarity metric",
abstract = "We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder (VAE) with a generative adversarial network (GAN) we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.",
author = "Larsen, {Anders Boesen Lindbo} and S{\o}nderby, {S{\o}ren Kaae} and Hugo Larochelle and Ole Winther",
year = "2016",
language = "English",
series = "JMLR: Workshop and Conference Proceedings",
publisher = "Microtome Publishing",
pages = "1558–1566",
editor = "Balcan, {Maria Florina} and Weinberger, {Kilian Q.}",
booktitle = "Proceedings of The 33rd International Conference on Machine Learning",
note = "null ; Conference date: 19-06-2016 Through 24-06-2016",

}

RIS

TY - GEN

T1 - Autoencoding beyond pixels using a learned similarity metric

AU - Larsen, Anders Boesen Lindbo

AU - Sønderby, Søren Kaae

AU - Larochelle, Hugo

AU - Winther, Ole

N1 - Conference code: 33

PY - 2016

Y1 - 2016

N2 - We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder (VAE) with a generative adversarial network (GAN) we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.

AB - We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder (VAE) with a generative adversarial network (GAN) we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.

M3 - Article in proceedings

AN - SCOPUS:84999041243

T3 - JMLR: Workshop and Conference Proceedings

SP - 1558

EP - 1566

BT - Proceedings of The 33rd International Conference on Machine Learning

A2 - Balcan, Maria Florina

A2 - Weinberger, Kilian Q.

Y2 - 19 June 2016 through 24 June 2016

ER -

ID: 171660204