2026
Evidential Deep Partial Label Learning to Quantify Disambiguation Uncertainty
CVPR 2026
Partial label learning (PLL) is a weakly supervised learning, where each instance is assigned a set of candidate labels and only one is true. However, due to potentially inaccurate annotations, existing PLL algorithms disambiguate labeling by minimizing the prediction loss, which leaves the model un