Variational Label Enhancement for Instance-Dependent Partial Label Learning.

Abstract

Partial label learning (PLL) is a form of weakly supervised learning, where each training example is linked to a set of candidate labels, among which only one label is correct. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, in practice, this assumption may not hold true, as the candidate labels are often instance-dependent. In this paper, we address the instance-dependent PLL problem and assume that each example is associated with a latent label distribution where the incorrect label with a high degree is more likely to be annotated as a candidate label. Motivated by this consideration, we propose two methods VALEN and MILEN, which train the predictive model via utilizing the latent label distributions recovered by the label enhancement process. Specifically, VALEN recovers the latent label distributions via inferring the variational posterior density parameterized by an inference model with the deduced evidence lower bound. MILEN recovers the latent label distribution by adopting the variational approximation to bound the mutual information among the latent label distribution, observed labels and augmented instances. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed methods.