Ambiguity-Induced Contrastive Learning for Instance-Dependent Partial Label Learning

Abstract

Partial label learning (PLL) learns from a typical weak supervision, where each training instance is labeled with a set of ambiguous candidate labels (CLs) instead of its exact ground-truth label. Most existing PLL works directly eliminate, rather than exploiting the label ambiguity, since they explicitly or implicitly assume that incorrect CLs are noise independent of the instance. While a more practical setting in the wild should be instance-dependent, namely, the CLs depend on both the true label and the instance itself, such that each CL may describe the instance from some sensory channel, thereby providing some noisy but really valid information about the instance. In this paper, we leverage such additional information acquired from the ambiguity and propose AmBiguity-induced contrastive LEarning (ABLE) under the framework of contrastive learning. Specifically, for each CL of an anchor, we select a group of samples currently predicted as that class as ambiguity-induced positives, based on which we synchronously learn a representor (RP) that minimizes the weighted sum of contrastive losses of all groups and a classifier (CS) that minimizes a classification loss. Although they are circularly dependent: RP requires the ambiguity-induced positives on-the-fly induced by CS, and CS needs the first half of RP as the representation extractor, ABLE still enables RP and CS to be trained simultaneously within a coherent framework. Experiments on benchmark datasets demonstrate its substantial improvements over state-of-the-art methods for learning from the instance-dependent partially labeled data.