3 papers
cs.LG2025
Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation
Tobias Fuchs, Nadja Klein
Real-world data is frequently noisy and ambiguous. In crowdsourcing, for example, human annotators may assign conflicting class labels to the same instances. Partial-label learning…
cs.LG2025
Robust Partial-Label Learning by Leveraging Class Activation Values
Tobias Fuchs, Florian Kalinke
Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances. Partial-label learning (PLL) is a weakly supervised le…
cs.LG2025
Partial-Label Learning with Conformal Candidate Cleaning
Tobias Fuchs, Florian Kalinke
Real-world data is often ambiguous; for example, human annotation produces instances with multiple conflicting class labels. Partial-label learning (PLL) aims at training a classif…