Reconstruction Error Aware Pruning for Accelerating Neural Networks

This paper presents a pruning method, Reconstruction Error Aware Pruning (REAP), to reduce the redundancy of convolutional neural network models for accelerating the inference. REAP is an extension of one of the state-of-the-art channel pruning methods. Our method takes 3 steps, (1) evaluating the importance of each channel based on the reconstruction error of the outputs in each convolutional layer, (2) pruning less important channels, (3) updating the remaining weights by the least squares method so as to reconstruct the outputs. By pruning with REAP, one can produce a fast and accurate model out of a large pretrained model. Besides, REAP saves us lots of time and efforts required for retraining the pruned model. As our method requires a large computational cost, we have developed an algorithm based on biorthogonal system to conduct the computation efficiently. In the experiments, we show that REAP can conduct pruning with smaller sacrifice of the model performances than several existing state-of-the-art methods such as CP [9], ThiNet [17], DCP [25], and so on.