Summary of Unipts: a Unified Framework For Proficient Post-training Sparsity, by Jingjing Xie et al.
UniPTS: A Unified Framework for Proficient Post-Training Sparsity
by Jingjing Xie, Yuxin Zhang, Mingbao Lin, Zhihang Lin, Liujuan Cao, Rongrong Ji
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel approach to efficient network sparsity called Post-training Sparsity (PTS). The authors aim to bridge the performance gap between traditional methods and existing PTS methods, which degrade significantly at high sparsity ratios. To achieve this, they introduce three key factors: a base-decayed sparsity objective, a reducing-regrowing search algorithm, and dynamic sparse training. These components are combined in a framework called UniPTS, which outperforms existing PTS methods across various benchmarks. For example, it improves the performance of POT (a recently proposed recipe) from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity ratio on ImageNet. The paper provides detailed results and code availability for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists developed a new way to make computer models more efficient by removing unnecessary parts while keeping the important information. They wanted to solve a problem where existing methods didn’t work well when they had limited data. To fix this, they came up with three key ideas: 1) using a special goal for sparsity that helps keep knowledge from the original model, 2) a search algorithm to find the best way to remove parts, and 3) training the model in a dynamic way that balances efficiency and stability. This new approach, called UniPTS, works much better than previous methods and can even improve performance by up to 65 times! |
Keywords
» Artificial intelligence » Pruning » Resnet
