Summary of Isomorphic Pruning For Vision Models, by Gongfan Fang et al.
Isomorphic Pruning for Vision Models
by Gongfan Fang, Xinyin Ma, Michael Bi Mi, Xinchao Wang
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 introduces Isomorphic Pruning, a novel approach to structured pruning in deep neural networks. The method focuses on removing redundant sub-structures and overcomes the challenge of assessing the relative importance of heterogeneous sub-structures. The authors demonstrate the effectiveness of their approach across various network architectures, including Vision Transformers and CNNs, achieving competitive performance while reducing model size. Isomorphic Pruning is based on an observation that different types of sub-structures exhibit distinct importance distributions, which can be leveraged for more reliable pruning. The method improves the accuracy of pre-trained models such as DeiT-Tiny and ConvNext-Tiny, while reducing computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a way to make artificial intelligence (AI) models like computer vision networks more efficient. These networks are made up of many small parts that work together, and some parts can be removed without affecting how well the network works. The authors found a new way to do this that works well with different types of networks. They tested it on two popular networks and showed that it can make them smaller and faster while keeping their accuracy the same. |
Keywords
* Artificial intelligence * Pruning
