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cs.LG2024
An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
Jiayu Hu, Senlin Shu, Beibei Li +2
Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-tr…
cs.LG2023
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
Senlin Shu, Shuo He, Haobo Wang +3
In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes…
cs.LG2021★ 1 cited
Multi-Class Classification from Single-Class Data with Confidences
Yuzhou Cao, Lei Feng, Senlin Shu +4
Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully lear…