Partial Label Learning by Semantic Difference Maximization

Partial Label Learning by Semantic Difference Maximization

Lei Feng, Bo An

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2294-2300. https://doi.org/10.24963/ijcai.2019/318

Partial label learning is a weakly supervised learning framework, in which each instance is provided with multiple candidate labels while only one of them is correct. Most of the existing approaches focus on leveraging the instance relationships to disambiguate the given noisy label space, while it is still unclear whether we can exploit potentially useful information in label space to alleviate the label ambiguities. This paper gives a positive answer to this question for the first time. Specifically, if two instances do not share any common candidate labels, they cannot have the same ground-truth label. By exploiting such dissimilarity relationships from label space, we propose a novel approach that aims to maximize the latent semantic differences of the two instances whose ground-truth labels are definitely different, while training the desired model simultaneously, thereby continually enlarging the gap of label confidences between two instances of different classes. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts.
Keywords:
Machine Learning: Classification
Machine Learning: Data Mining