ProPaLL: Probabilistic Partial Label Learning
This addresses a weakly supervised learning problem for machine learning practitioners, offering an incremental improvement in method efficiency and performance.
The paper tackles partial label learning, where each training instance has a set of candidate labels with only one true label, by introducing ProPaLL, a probabilistic approach that simplifies training, improves performance, and is applicable to any deep architecture, with experiments showing it outperforms existing methods.
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.