Layer Pruning for Obtaining Shallower ResNets

Network pruning has become an effective scheme to cut down the network complexity and speed up the inference. Current work mainly focuses on filter pruning, which deletes filters in a structured way. Although the calculation amount is reduced after pruning, the acceleration effect is not obvious. In this letter, we present a layer pruning method to obtain shallower ResNets (LPSR) and significantly reduce the inference latency. Taking advantage of the characteristics of the residual module, we disconnect the unimportant residual mapping and reserve only the identity mapping, thereby equivalently removing the corresponding residual module and achieving the purpose of layer pruning. We adopt Taylor expansion to estimate the impact of disconnecting BN layers in the residual mapping. These impacts are then normalized and used to identify unimportant residual blocks to prune. Comprehensive experimental results demonstrate that LPSR not only reduces the number of parameters and calculations, but also significantly speed up the inference. LPSR improves the accuracy of ResNet-56 on CIFAR-10 from 93.21% to 93.40%, while reducing 44.71% parameters, 52.68% calculations and 44.89% inference latency.