@article{Yan_Guo_2020, title={Partial Label Learning with Batch Label Correction}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6132}, DOI={10.1609/aaai.v34i04.6132}, abstractNote={<p>Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels, among which only one is the true label. In this paper, we propose a simple but effective batch-based partial label learning algorithm named PL-BLC, which tackles the partial label learning problem with batch-wise label correction (BLC). PL-BLC dynamically corrects the label confidence matrix of each training batch based on the current prediction network, and adopts a MixUp data augmentation scheme to enhance the underlying true labels against the redundant noisy labels. In addition, it introduces a teacher model through a consistency cost to ensure the stability of the batch-based prediction network update. Extensive experiments are conducted on synthesized and real-world partial label learning datasets, while the proposed approach demonstrates the state-of-the-art performance for partial label learning.</p>}, number={04}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yan, Yan and Guo, Yuhong}, year={2020}, month={Apr.}, pages={6575-6582} }