Poster
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson · Fabian Mentzer · Michael Tschannen · Lukas Cavigelli · Radu Timofte · Luca Benini · Luc V Gool

Mon Dec 4th 06:30 -- 10:30 PM @ Pacific Ballroom #133 #None
in Posters Mon »

We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both.

Keywords: Deep Learning · Representation Learning · Optimization for Deep Networks

Author Information

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.

Fabian Mentzer (ETH Zurich)
Michael Tschannen (ETH Zurich)
Lukas Cavigelli (ETH Zurich)
Radu Timofte (ETH Zurich)
Luca Benini (ETH Zurich)
Luc V Gool (Computer Vision Lab, ETH Zurich)

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