Binary Quantizer

  • Vahid Partovi Nia
  • Mouloud Belbahri

Abstract

One-bit quantization is a general tool to execute a complex model,
such as deep neural networks, on a device with limited resources,
such as cell phones. Naively compressing weights into one bit
yields an extensive accuracy loss. One-bit models, therefore, re-
quire careful re-training. Here we introduce a class functions de-
vised to be used as a regularizer for re-training one-bit models. Us-
ing a regularization function, specifically devised for binary quanti-
zation, avoids heuristic touch of the optimization scheme and saves
considerable coding effort.

Published
2018-12-24
How to Cite
Nia, V., & Belbahri, M. (2018). Binary Quantizer. Journal of Computational Vision and Imaging Systems, 4(1), 3. Retrieved from https://openjournals.uwaterloo.ca/index.php/vsl/article/view/334