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cs.LG2023
Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
Shuo He, Lei Feng, Guowu Yang
Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated…
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…