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Arxiv
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Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
- In Continual Learning (CL), there has been limited research on Multi-Label Learning (MLL).
- MLL datasets are often class-imbalanced, making it challenging in CL.
- To optimize Macro-AUC in MLCL, a new memory replay-based method called RLDAM is proposed.
- Experimental results demonstrate the effectiveness of the proposed method over baselines.
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