PARTIAL LABEL UNSUPERVISED DOMAIN ADAPTA-TION WITH CLASS-PROTOTYPE ALIGNMENT

Abstract

Partial label learning (PLL) tackles the problem where each instance is associated with a set of candidate labels, only one of which is the ground-truth label. Most existing PLL approaches assume that both the training and test sets share an identical data distribution. However, this assumption does not hold in many realworld scenarios where the training and test data come from different distributions. In this paper, we formalize this learning scenario as a new problem called partial label unsupervised domain adaptation (PLUDA). To address this challenging PLUDA problem, we propose a novel Prototype Alignment based PLUDA method named PAPLUDA, which dynamically refines the pseudo-labels of instances from both the source and target domains by consulting the outputs of a teacher-student model in a moving-average manner, and bridges the cross-domain discrepancy through inter-domain class-prototype alignment. In addition, a teacher-student model based contrastive regularization is deployed to enhance prediction stability and hence improve the class-prototypes in both domains for PLUDA. Comprehensive experimental results demonstrate that PAPLUDA achieves state-of-the-art performance on the widely used benchmark datasets.

1. INTRODUCTION

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training instance is assigned a candidate label set, only one of which is valid. PLL has gained increasing attention from the research community due to its effectiveness in reducing annotation costs in various real-world scenarios, such as face naming (Hüllermeier & Beringer, 2006) , web mining (Luo & Orabona, 2010), and ecoinformatics (Liu & Dietterich, 2014) . Nevertheless, standard PLL assumes the training and test data are sampled from the same distribution. With this assumption, a model learned from the training data is expected to generalize well on the test data. However, this assumption does not hold in many real-world scenarios where the training and test data come from different distributions-e.g., the training and test data are collected from different sources, or we have an outdated training set due to the fact that data always change over time. In such cases, there would be a discrepancy between the training and test data distributions, and hence naively adopting the off-the-shelf PLL models can lead to significant test performance degradation. Meanwhile, the unavailability of the ground-truth labels prevents the deployment of existing unsupervised domain adaptation (UDA) methods (Tzeng et al., 2017; Dong et al., 2021; Na et al., 2021; Shen et al., 2022) . We formalize this new learning scenario of PLL with training-test distribution gaps as a partial label unsupervised domain adaptation (PLUDA) problem. By integrating the challenges of both PLL and UDA problems, the PLUDA problem has the following characteristics: (1) the source and target domains have different distributions but share the same set of classes; (2) data in the source domain have only partial labels-each instance is associated with a candidate label set, while the target domain only has unlabeled data; (3) the candidate label set for each source instance can contain both the ground-truth and irrelevant noisy labels, while labels outside of the candidate set are true negative labels. The goal of the PLUDA task is to learn a domain-invariant prediction model from the partial-label source domain that can generalize well in the unlabeled target domain. Although both PLL and UDA have been studied intensively in the literature, to the best of our knowledge, there is no research yet to address the integrated challenges of PLUDA in a unified 1

