Yiu‐ming Cheung

Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction

2024 · IEEE Transactions on Neural Networks and Learning Systems

Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using label enhancement techniques, researchers have …

PLOOD: Partial Label Learning with Out-of-distribution Objects

2024 · arXiv (Cornell University)

Existing Partial Label Learning (PLL) methods posit that training and test data adhere to the same distribution, a premise that frequently does not hold in practical application where Out-of-Distribution (OOD) objects are present. We introduce …