ProPaLL: Probabilistic Partial Label Learning

Łukasz Struski; Jacek Tabor; Bartosz Zieliński

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arXiv · 2208.09931

ProPaLL: Probabilistic Partial Label Learning

Łukasz Struski·Jacek Tabor·Bartosz Zieliński

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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.

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Łukasz Struski, Jacek Tabor, Bartosz Zieliński. 2022-08-21. ProPaLL: Probabilistic Partial Label Learning. https://arxiv.org/abs/2208.09931

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