Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
C.O. Pene (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Lydia Chen – Mentor (TU Delft - Data-Intensive Systems)
S. Ghiassi – Mentor (TU Delft - Data-Intensive Systems)
T. Younesian – Mentor (TU Delft - Data-Intensive Systems)
F.A. Kuipers – Graduation committee member (TU Delft - Embedded Systems)
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Abstract
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive, but also many of the actual labels are corrupted or missing due to the automated or non-expert annotation techniques. Noisy label data decrease the prediction performance drastically. In this paper, we propose a novel Gold Asymmetric Loss Correction with Single-Label Regulators (GALC-SLR) that operates robust against noisy labels. GALC-SLR estimates the noise confusion matrix using single-label samples, then constructs an asymmetric loss correction via estimated confusion matrix to avoid overfitting to the noisy labels. Empirical results show that our method outperforms the state-of-the-art original asymmetric loss multi-label classifier under all corruption levels, showing mean average precision improvement up to 28.67\% on a real-world dataset of MS-COCO, yielding a better generalization of the unseen data and increased prediction performance.