Summary of Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance, by Mostafa Hussien et al.
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance
by Mostafa Hussien, Mahmoud Afifi, Kim Khoa Nguyen, Mohamed Cheriet
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 an innovative pruning technique for neural networks, which reduces model size and complexity while maintaining performance. The approach relies on activation statistics and information theory to identify and remove weights with minimal contributions to neuron outputs. By building a distribution of weight contributions across the dataset and using its parameters to guide the pruning process, the method achieves state-of-the-art results on multiple datasets and network architectures. Furthermore, the authors propose a Pruning-aware Training strategy that incorporates an additional regularization term to enhance the effectiveness of their method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making neural networks smaller and more efficient without losing their ability to perform well. The researchers developed a new way to reduce the size of these networks by looking at how much each part contributes to the network’s output. They found that some parts don’t contribute much, so they can be removed safely. This helps solve the problem of being able to run large neural networks on devices with limited resources. The authors tested their method on several different datasets and showed it works better than other methods. |
Keywords
» Artificial intelligence » Pruning » Regularization
