Summary of Center-sensitive Kernel Optimization For Efficient On-device Incremental Learning, by Dingwen Zhang et al.
Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning
by Dingwen Zhang, Yan Li, De Cheng, Nannan Wang, Junwei Han
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A novel edge-friendly incremental learning framework is proposed in this paper, tackling the issue of catastrophic forgetting in on-device training. The current focus on efficient training without considering forgetting limits the model’s ability to learn from new data. To address this, a simple yet effective framework is designed, leveraging the empirical finding that center kernel elements are key for maximizing knowledge intensity. A center-sensitive kernel optimization framework and dynamic channel element selection strategy are also proposed to reduce computation complexity. Experimental results show significant potential for on-device incremental learning, achieving an average accuracy boost of 38.08% with reduced memory and approximate computation compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in making machines smarter while using limited resources. When machines learn from new data, they often forget what they learned before. The researchers found that some parts of the machine learning model are more important than others for keeping this information. They designed a new way to update these important parts, making it faster and more efficient. This is important because it means devices like smartphones can learn and improve without needing a lot of power or memory. |
Keywords
» Artificial intelligence » Machine learning » Optimization
