Summary of Decay Pruning Method: Smooth Pruning with a Self-rectifying Procedure, by Minghao Yang et al.
Decay Pruning Method: Smooth Pruning With a Self-Rectifying Procedure
by Minghao Yang, Linlin Gao, Pengyuan Li, Wenbo Li, Yihong Dong, Zhiying Cui
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel smooth pruning approach called Decay Pruning Method (DPM) that addresses accuracy drops caused by abrupt network changes and loss of information from pruned structures. DPM consists of two key components: Smooth Pruning, which gradually reduces redundant structures over multiple steps with ongoing optimization, and Self-Rectifying, which rectifies sub-optimal pruning based on gradient information. The approach demonstrates strong generalizability and can be easily integrated with various existing pruning methods. Experimental results show consistent improvements in performance compared to the original pruning methods, along with further reductions of FLOPs in most scenarios. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make computer models work better without losing important parts. Current ways of making models smaller often cause problems and lower their performance. The new method, called Decay Pruning Method (DPM), makes changes gradually over time to keep the model’s performance high. It also checks what it did and fixes any mistakes. This method can be used with many other ways of making models smaller and works well. Tests show that this method helps make models better without using too much computer power. |
Keywords
» Artificial intelligence » Optimization » Pruning