Trusted Loss Correction for Noisy Multi-Label Learning
Proceedings of The 14th Asian Conference on Machine Learning, PMLR 189:343-358, 2023.
Noisy and corrupted labels are shown to significantly undermine the performance of multi-label learning, which has multiple labels in each image. Correcting the loss via a label corruption matrix is effective in improving the robustness of single-label classification against noisy labels. However, estimating the corruption matrix for multi-label problems is no mean feat due to the unbalanced distributions of labels and the presence of multiple objects that may be mapped into the same labels. In this paper, we propose a robust multi-label classifier against label noise, TLCM, which corrects the loss based on a corruption matrix estimated on trusted data. To overcome the challenge of unbalanced label distribution and multi-object mapping, we use trusted single-label data as regulators to correct the multi-label corruption matrix. Empirical evaluation on real-world vision and object detection datasets, i.e., MS-COCO, NUS-WIDE, and MIRFLICKR, shows that our method under medium (30%) and high (60%) corruption levels outperforms state-of-the-art multi-label classifier (ASL) and noise-resilient multi-label classifier (MPVAE), by on average 12.5% and 26.3% mean average precision (mAP) points, respectively.