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Poster
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen · Eirikur Agustsson · Mario Lucic

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #83
in Thu Poster Session A »

We propose and study the problem of distribution-preserving lossy compression. Motivated by recent advances in extreme image compression which allow to maintain artifact-free reconstructions even at very low bitrates, we propose to optimize the rate-distortion tradeoff under the constraint that the reconstructed samples follow the distribution of the training data. The resulting compression system recovers both ends of the spectrum: On one hand, at zero bitrate it learns a generative model of the data, and at high enough bitrates it achieves perfect reconstruction. Furthermore, for intermediate bitrates it smoothly interpolates between learning a generative model of the training data and perfectly reconstructing the training samples. We study several methods to approximately solve the proposed optimization problem, including a novel combination of Wasserstein GAN and Wasserstein Autoencoder, and present an extensive theoretical and empirical characterization of the proposed compression systems.

Keywords: Adversarial Networks · Generative Models · Representation Learning
[ Paper]  [ Poster] 

Author Information

Michael Tschannen (ETH Zurich)
Eirikur Agustsson (ETH Zurich)

I am a PhD student at the [Computer Vision Lab](http://www.vision.ee.ethz.ch) of [ETH Zurich](https://www.ethz.ch/en.html), under the supervision of [Prof. Luc Van Gool](https://scholar.google.ch/citations?user=TwMib_QAAAAJ&hl=en&oi=ao). Previously, I received a MSc degree in Electrical Engineering and Information Technology from ETH Zurich and a double BSc degree in Mathematics and Electrical Engineering from the University of Iceland. My main research interests include deep learning for data compression, regression & classification.

Mario Lucic (Google Brain)

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