# Cluster Regularized Quantization for Deep Networks Compression

## TL;DR

This paper introduces Cluster Regularized Quantization (CRQ), a method that compresses deep neural networks to ternary weights with minimal accuracy loss by regularizing weights during retraining.

## Contribution

The paper proposes a novel cluster regularization technique that enables effective low-bit quantization of DNNs during retraining, reducing quantization error.

## Key findings

- Effective ternary quantization with minimal accuracy drop
- Reduces model size and computational cost
- Demonstrated on benchmark datasets

## Abstract

Deep neural networks (DNNs) have achieved great success in a wide range of computer vision areas, but the applications to mobile devices is limited due to their high storage and computational cost. Much efforts have been devoted to compress DNNs. In this paper, we propose a simple yet effective method for deep networks compression, named Cluster Regularized Quantization (CRQ), which can reduce the presentation precision of a full-precision model to ternary values without significant accuracy drop. In particular, the proposed method aims at reducing the quantization error by introducing a cluster regularization term, which is imposed on the full-precision weights to enable them naturally concentrate around the target values. Through explicitly regularizing the weights during the re-training stage, the full-precision model can achieve the smooth transition to the low-bit one. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method.