Wavelet Feature Maps Compression for Image-to-Image CNNs

Shahaf E. Finder, Yair Zohav, Maor Ashkenazi, Eran Treister

Keywords: Convolutional Neural Networks, Quantization, Wavelet Transform

TL;DR: We propose a novel method for compressing feature maps within CNNs to reduce computational costs with minimal performance degradation.

Abstract: Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well for classification, it may cause severe performance degradation in image-to-image tasks such as semantic segmentation and depth estimation. In this paper, we propose Wavelet Compressed Convolution (WCC)---a novel approach for high-resolution activation maps compression integrated with point-wise convolutions, which are the main computational cost of modern architectures. To this end, we use an efficient and hardware-friendly Haar-wavelet transform, known for its effectiveness in image compression, and define the convolution on the compressed activation map. We experiment with various tasks that benefit from high-resolution input. By combining WCC with light quantization, we achieve compression rates equivalent to 1-4bit activation quantization with relatively small and much more graceful degradation in performance. Our code is available at https://github.com/BGUCompSci/WaveletCompressedConvolution.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2205.12268/code)