Summary of Sharpness-diversity Tradeoff: Improving Flat Ensembles with Sharpbalance, by Haiquan Lu et al.
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
by Haiquan Lu, Xiaotian Liu, Yefan Zhou, Qunli Li, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 proposed study delves into the intricate relationship between sharpness and diversity within deep ensembles, shedding light on their critical role in achieving robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data. The investigation reveals a trade-off between these two factors: minimizing sharpness tends to diminish individual member diversity, compromising ensemble performance. To address this issue, the authors introduce SharpBalance, a novel training approach that balances sharpness and diversity within ensembles, demonstrating theoretical and empirical improvements in various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how deep ensembles can be improved by understanding the balance between sharpness and diversity. This is important because it helps make sure the ensemble performs well even when faced with new, unknown data. The researchers found that if they made the individual models more precise (sharp), they would become less diverse, which is bad for performance. They then developed a way to train ensembles so that both precision and diversity are balanced, leading to better results. |
Keywords
» Artificial intelligence » Generalization » Precision
