Wavelet Feature Maps Compression for Image-to-Image CNNs. (arXiv:2205.12268v3 [cs.CV] UPDATED)

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 …

arxiv cnns compression feature image maps wavelet