SIECP: Neural Network Channel Pruning based on Sequential Interval Estimation

Pruning is widely regarded as an effective neural network compression and acceleration method, which can significantly reduce model parameters and speed up inference. This paper proposes a novel network Channel Pruning method based on Sequential Interval Estimation (SIECP). Our method mainly solves the problem that existing methods need to sample and evaluate a large number of sub-structures or introduce a large number of parameters, which leads to slow search. Specifically, we divide the entire channel number search process into multiple stages. In each stage, we divide the channel range that needs to be estimated into multiple channel intervals by grouping and use gradient descent to optimize the number of reserved channels. At the same time, the distribution of the number of channels is counted in each interval. At the end of each search stage, we select the channel interval with the highest frequency for further search to gradually narrow the search range until the final number of channels is determined. Then, the unpruned network is pruned according to the number of channels in each layer of the network to obtain the pruned network. Extensive experiments using ResNet and MobileNet V2 as backbones on CIFAR10, CIAFR100 and Tiny-ImageNet datasets are conducted to demonstrate the effectiveness of our method.