# Wavelet Feature Maps Compression for Image-to-Image CNNs Source: [Wavelet Feature Maps Compression for Image-to-Image CNNs]() Collection index: [llms.txt]() Data updated: 2026-01-01T15:27:46.775Z Authors: Shahaf E. Finder, Yair Zohav, Maor Ashkenazi, Eran Treister Published: 2022-05-24T20:29:19.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on MS COCO (val)](): Object Detection; MS-COCO (val); mAP: 0.3144. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()