Small Network for Lightweight Task in Computer Vision: A Pruning Method Based on Feature Representation

Yisu Ge, Shufang Lu, Fei Gao, Paolo Gastaldo
2021 Computational Intelligence and Neuroscience  
Many current convolutional neural networks are hard to meet the practical application requirement because of the enormous network parameters. For accelerating the inference speed of networks, more and more attention has been paid to network compression. Network pruning is one of the most efficient and simplest ways to compress and speed up the networks. In this paper, a pruning algorithm for the lightweight task is proposed, and a pruning strategy based on feature representation is
more » ... Different from other pruning approaches, the proposed strategy is guided by the practical task and eliminates the irrelevant filters in the network. After pruning, the network is compacted to a smaller size and is easy to recover accuracy with fine-tuning. The performance of the proposed pruning algorithm is validated on the acknowledged image datasets, and the experimental results prove that the proposed algorithm is more suitable to prune the irrelevant filters for the fine-tuning dataset.
doi:10.1155/2021/5531023 pmid:33959156 pmcid:PMC8075670 fatcat:gwzannnunnh5ri3icarhyhzr5e