Instance-Dependent Partial Label Learning with Identifiable Causal Representations

Abstract

Partial label learning (PLL) deals with the problem where each training example is annotated with a set of candidate labels, among which only one is true. In real-world scenarios, the candidate labels are generally dependent to the instance features. However, existing PLL methods focus solely on classification accuracy, whereas the possibility of exploiting the dependency for causal representation learning remains unexplored. In this paper, we investigate learning causal representations under the PLL paradigm and propose a novel framework which learns identifiable latent factors up to permutation, scaling and translation. Qualitative and quantitative experiments confirmed the effectiveness of this approach.