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arXiv · 1704.00648

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 Van Gool
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Abstract

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.

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Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool. 2017-04-03. Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. https://arxiv.org/abs/1704.00648

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