Papers with multi-label data augmentation
Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation (2024.naacl-long)
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| Challenge: | Existing methods for multi-label data augmentation have been ineffective, authors say . a mere 1.5% of labels have more than 100 training instances, a problem that persists for years . |
| Approach: | They propose a new paradigm for multi-label data augmentation called Label Creative Generation . they propose tail-driven conditional augmentation with tail-based sampling and label-conditioned generation . |
| Outcome: | The proposed approach has shown a 10% increase in PSP@1 across three datasets . it effectively mitigates the long-tail effect and enhances model performance . |