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cs.LG2023
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Hwanjun Song, Minseok Kim, Jae-Gil Lee
Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these c…
cs.LG2023★ 4 cited
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Dongmin Park, Seola Choi, Doyoung Kim +2
Data pruning, which aims to downsize a large training set into a small informative subset, is crucial for reducing the enormous computational costs of modern deep learning. Though…