Leveraged Asymmetric Loss with Disambiguation for Multi-label Recognition with One-Positive Annotations

Jingyi Cui, Tao Huang, Hanyuan Hang, Yisen Wang, James Kwok

Abstract: In the problem of multi-label learning from single positive labels (SPL), we learn the potential multiple labels from one observable single positive annotation. Despite many efforts to solve this problem, an effective algorithm with sound theoretical understanding is still in need. In this paper, we propose a novel loss function for the SPL problem, called leveraged asymmetric loss with disambiguation (LASD), where we introduce a pair of leverage parameters to address the severe negative-positive imbalance. From the theoretical perspective, we analyze the SPL problem, for the first time, from the perspective of risk consistency, which links the SPL loss with losses for ordinary multi-label classification. We prove the consistency of our proposed LASD loss to the cost-sensitive Hamming loss, which provides guidance to the empirical choice of our proposed leverage parameters. In experiments, we demonstrate the effectiveness of our proposed LASD loss function over other state-of-the-art methods and empirically verify our theoretical results.

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