ProPaLL: Probabilistic Partial Label Learning

Łukasz Struski; Jacek Tabor; Bartosz Zieliński

ProPaLL: Probabilistic Partial Label Learning

Machine Learning 2022-08-23 v1 Artificial Intelligence

Authors: Łukasz Struski , Jacek Tabor , Bartosz Zieliński

Abstract

Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilistic approach to this problem, which has at least three advantages compared to the existing approaches: it simplifies the training process, improves performance, and can be applied to any deep architecture. Experiments conducted on artificial and real-world datasets indicate that ProPaLL outperforms the existing approaches.

Keywords

semi-supervised learning multi-label classification active learning

Cite

@article{arxiv.2208.09931,
  title  = {ProPaLL: Probabilistic Partial Label Learning},
  author = {Łukasz Struski and Jacek Tabor and Bartosz Zieliński},
  journal= {arXiv preprint arXiv:2208.09931},
  year   = {2022}
}

ProPaLL: Probabilistic Partial Label Learning

Abstract

Keywords

Cite

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