Wavelet Feature Maps Compression for Image-to-Image CNNs

• URL: http://arxiv.org/abs/2205.12268v1
• Date: Tue, 24 May 2022 20:29:19 GMT
• Title: Wavelet Feature Maps Compression for Image-to-Image CNNs
• Authors: Shahaf E. Finder, Yair Zohav, Maor Ashkenazi, Eran Treister
• Abstract summary: We propose a novel approach for high-resolution activation maps compression integrated with point-wise convolutions. We achieve compression rates equivalent to 1-4bit activation quantization with relatively small and much more graceful degradation in performance.
• Score: 3.1542695050861544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 on various tasks, that benefit from high-resolution input, and 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.

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