AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference

作者：

Highlights：

• Filter selection and model fine-tuning are integrated into a single end-to-end trainable framework.

• Adaptive compression ratio and multi-layer compression.

• Good generalization ability.

摘要

•Filter selection and model fine-tuning are integrated into a single end-to-end trainable framework.•Adaptive compression ratio and multi-layer compression.•Good generalization ability.

论文关键词：Neural network pruning,Model compression,CNN acceleration

论文评审过程：Received 7 September 2019, Revised 15 May 2020, Accepted 16 May 2020, Available online 25 May 2020, Version of Record 15 June 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107461