Summary of Towards Macro-auc Oriented Imbalanced Multi-label Continual Learning, by Yan Zhang et al.
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
by Yan Zhang, Guoqiang Wu, Bingzheng Wang, Teng Pang, Haoliang Sun, Yilong Yin
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a new memory replay-based method to tackle the imbalance issue in Multi-Label Learning (MLL) for Continual Learning (CL). The authors focus on optimizing Macro-AUC, a widely used measure in MLL. To achieve this, they introduce a Reweighted Label-Distribution-Aware Margin (RLDAM) loss and a Weight Retain Updating (WRU) strategy to maintain the numbers of positive and negative instances in memory. Theoretically, the authors analyze the generalization performance of their method in both batch MLL and MLCL settings. Experimental results demonstrate the effectiveness of their approach over several baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make machine learning models work better when they’re faced with new data that’s different from what they’ve seen before. Right now, there isn’t much research on how to deal with this problem in certain types of datasets. The authors propose a new method that uses memories to help the model learn and adapt to these new datasets. They also come up with a way to measure how well their method works, which is important because it shows that their approach is effective. |
Keywords
» Artificial intelligence » Auc » Continual learning » Generalization » Machine learning
