MWQ: Multiscale Wavelet Quantized Neural Networks

TL;DR

This paper introduces MWQ, a multiscale wavelet quantization method that decomposes neural network data into frequency components to reduce information loss and improve performance on resource-constrained devices.

Contribution

The paper proposes a novel wavelet-based quantization approach that considers multiscale frequency information, enhancing neural network performance and flexibility in quantization tasks.

Findings

MWQ improves model compression efficiency.

It enhances the representation ability of quantized networks.

Experimental results outperform existing quantization methods.

Abstract

Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardware (e.g., mobile phones and embedded devices). The existing quantization methods mainly consider the numerical elements of the weights and activation values, ignoring the relationship between elements. The decline of representation ability and information loss usually lead to the performance degradation. Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method. This method decomposes original data into multiscale frequency components by wavelet transform, and then quantizes the components of different scales, respectively. It exploits the multiscale frequency and spatial information to alleviate the information loss caused by quantization in the spatial domain. Because of the flexibility of MWQ, we demonstrate three applications (e.g., model compression, quantized network optimization, and information enhancement) on the ImageNet and COCO datasets. Experimental results show that our method has stronger representation ability and can play an effective role in quantized neural networks.