ProPaLL: Probabilistic Partial Label Learning

• URL: http://arxiv.org/abs/2208.09931v1
• Date: Sun, 21 Aug 2022 17:47:44 GMT
• Title: ProPaLL: Probabilistic Partial Label Learning
• Authors: {\L}ukasz Struski, Jacek Tabor, Bartosz Zieli\'nski
• Abstract summary: 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. Experiments conducted on artificial and real-world datasets indicate that ProPaLL outperforms the existing approaches.
• Score: 14.299728437638512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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