Papers › Network Pruning via Performance Maximization

Network Pruning via Performance Maximization

19 Jun 2021CVPR 2021 1archive 2025-07-28

Shangqian Gao, Feihu Huang, Weidong Cai, Heng Huang

Channel pruning is a class of powerful methods for model compression. When pruning a neural network, it's ideal to obtain a sub-network with higher accuracy. However, a sub-network does not necessarily have high accuracy with low classification loss (loss-metric mismatch). In the paper, we first consider the loss-metric mismatch problem for pruning and propose a novel channel pruning method for Convolutional Neural Networks (CNNs) by directly maximizing the performance (i.e., accuracy) of sub-networks. Specifically, we train a stand-alone neural network to predict sub-networks' performance and then maximize the output of the network as a proxy of accuracy to guide pruning. Training such a performance prediction network efficiently is not an easy task, and it may potentially suffer from the problem of catastrophic forgetting and the imbalance distribution of sub-networks. To deal with this challenge, we introduce a corresponding episodic memory to update and collect sub-networks during the pruning process. In the experiment section, we further demonstrate that the gradients from the performance prediction network and the classification loss have different directions. Extensive experimental results show that the proposed method can achieve state-of-the-art performance with ResNet, MobileNetV2, and ShuffleNetV2+ on ImageNet and CIFAR-10.

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Model Compression Network Pruning

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1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Depthwise Convolution Depthwise Separable Convolution Global Average Pooling Inverted Residual Block Kaiming Initialization Max Pooling Pointwise Convolution Pruning ReLU Residual Block Residual Connection

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