Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

TL;DR

This paper introduces highly efficient algorithms for CNN forward and backward propagation tailored for pixelwise image classification, significantly reducing redundant computations and accelerating processing on GPUs.

Contribution

The paper proposes novel d-regularly sparse kernels that eliminate redundant convolution and pooling computations, enabling fast, memory-efficient CNN processing for pixelwise tasks.

Findings

Speed up patch-by-patch scanning over 1500 times

Efficiency increases with larger images and patches

Exact results equivalent to traditional patch scanning

Abstract

We present highly efficient algorithms for performing forward and backward propagation of Convolutional Neural Network (CNN) for pixelwise classification on images. For pixelwise classification tasks, such as image segmentation and object detection, surrounding image patches are fed into CNN for predicting the classes of centered pixels via forward propagation and for updating CNN parameters via backward propagation. However, forward and backward propagation was originally designed for whole-image classification. Directly applying it to pixelwise classification in a patch-by-patch scanning manner is extremely inefficient, because surrounding patches of pixels have large overlaps, which lead to a lot of redundant computation. The proposed algorithms eliminate all the redundant computation in convolution and pooling on images by introducing novel d-regularly sparse kernels. It generates exactly the same results as those by patch-by-patch scanning. Convolution and pooling operations with such kernels are able to continuously access memory and can run efficiently on GPUs. A fraction of patches of interest can be chosen from each training image for backward propagation by applying a mask to the error map at the last CNN layer. Its computation complexity is constant with respect to the number of patches sampled from the image. Experiments have shown that our proposed algorithms speed up commonly used patch-by-patch scanning over 1500 times in both forward and backward propagation. The speedup increases with the sizes of images and patches.