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Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks

Abstract · Apr 3, 2017 15:39 ·

cs-lg cs-cv

Arxiv Abstract

  • Eirikur Agustsson
  • Fabian Mentzer
  • Michael Tschannen
  • Lukas Cavigelli
  • Radu Timofte
  • Luca Benini
  • Luc Van Gool

In this work 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 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 state-of-the-art results for both.

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