  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.
