Decompositional Generation Process for Instance-Dependent Partial Label Learning

TL;DR

This paper introduces a novel instance-dependent partial label learning method that models the candidate label generation process as a two-step, decomposed probabilistic process, improving learning accuracy.

Contribution

It proposes a new PLL approach that explicitly models the instance-dependent label generation process using decomposed probability distributions, addressing limitations of previous methods.

Findings

Outperforms existing PLL methods on benchmark datasets

Effective in real-world noisy labeling scenarios

Validated through extensive experiments

Abstract

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way. However, these approaches usually do not perform as well as expected due to the fact that the generation process of the candidate labels is always instance-dependent. Therefore, it deserves to be modeled in a refined way. In this paper, we consider instance-dependent PLL and assume that the generation process of the candidate labels could decompose into two sequential parts, where the correct label emerges first in the mind of the annotator but then the incorrect labels related to the feature are also selected with the correct label as candidate labels due to uncertainty of labeling. Motivated by this consideration, we propose a novel PLL method that performs Maximum A Posterior (MAP) based on an explicitly modeled generation process of candidate labels via decomposed probability distribution models. Extensive experiments on manually corrupted benchmark datasets and real-world datasets validate the effectiveness of the proposed method. Source code is available at https://github.com/palm-ml/idgp.