Partial Label Causal Representation Learning for Instance-Dependent Supervision and Domain Generalization
Year: 2025
Type: conference-paper
Abstract: Partial label learning (PLL) addresses situations where each training example is associated with a set of candidate labels, among which only one corresponds to the true class label. As the candidate labels often come from crowdsourced workers, their generation is inherently dependent on the features of the instance. Existing PLL methods primarily aim to resolve these ambiguous labels to enhance classification accuracy, overlooking the opportunity to use this feature dependency for causal representation learning. This focus on accuracy can make PLL systems vulnerable to stylistic variations and shifts in domain. In this paper, we explore the learning of causal representations within an instance-dependent PLL framework, introducing a new approach that uncovers identifiable latent representations. By separating content from style in the identified causal representation, we introduce CausalPLL+, an algorithm for instance-dependent PLL based on causal representation. Our algorithm performs exceptionally well in terms of both classification accuracy and generalization robustness. Qualitative and quantitative experiments on instance-dependent PLL benchmarks and domain generalization tasks verify the effectiveness of our approach.
Authors: Yizhi Wang, Weijia Zhang, Min-Ling Zhang
Institutions: Southeast University, University of Newcastle Australia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409363576