Label-Free Network Pruning via Reinforcement Learning
LIU Huidong1, DU Fang1,2, YU Zhenhua1,2, SONG Lijuan1,2
1. School of Information Engineering, Ningxia University, Yinchuan 750021 2. Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan 750021
Abstract To remove redundant structures from deep neural networks and find a network structure with a good balance between capability and complexity, a label-free global compression learning method(LFGCL) is proposed. A global pruning strategy is learned based on the network architecture representation to effectively avoid the appearance of the suboptimal compression rate owing to network pruning in a layer-by-layer manner. LFGCL is independent from data labels during pruning, and the network architecture is optimized by outputting similar features with the baseline network. The deep deterministic policy gradient algorithm is applied to explore the optimal network structure by inferring the compression ratio of all layers through reinforcement learning. Experiments on multiple datasets show that LFGCL generates better performance.
Fund:National Natural Science Foundation of China(No.61901238), Natural Science Foundation of Ningxia Province(No.2018AAC03020,2018AAC03025)
Corresponding Authors:
YU Zhenhua, Ph.D., lecturer. His research interests include machine learning, computer vision and bioinformatics.
About author:: LIU Huidong, master student. His research interests include network compression and reinforcement learning.DU Fang, Ph.D., professor. Her research interests include big data management and artificial intelligence.SONG Lijuan, Ph.D., associate professor. Her research interests include image proce-ssing and computer vision.
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