Summary of A Comprehensive Study Of Structural Pruning For Vision Models, by Changhao Li et al.
A Comprehensive Study of Structural Pruning for Vision Models
by Changhao Li, Haoling Li, Mengqi Xue, Gongfan Fang, Sheng Zhou, Zunlei Feng, Huiqiong Wang, Mingli Song, Jie Song
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper presents PruningBench, a comprehensive benchmark for evaluating structural pruning techniques in machine learning models. The authors address the lack of standardized benchmarks and metrics in this area, providing a unified framework for evaluating 16 existing pruning methods across various models and tasks. The benchmark includes easily implementable interfaces for future researchers to integrate their work into leaderboards, enabling customizing pruning tasks and reproducing results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a standardized way to compare different structural pruning techniques. This helps the field move forward by comparing how well each method works on different types of models and tasks. The benchmark includes many different methods and makes it easy for others to test their own methods against these existing ones. |
Keywords
» Artificial intelligence » Machine learning » Pruning
