mlsanityData to 14 Oct 2026

ProPaLL: Probabilistic Partial Label Learning

arXiv 2208.09931 ↗First posted 21 Aug 2022cs.LG cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 21 Aug 2022.

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21 of 43 references.

References · 21

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9 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024ProPML: Probability Partial Multi-label Learningcited by 1