Robust Partial-Label Learning by Leveraging Class Activation Values

TL;DR

This paper introduces a robust partial-label learning method that leverages class activation values and subjective logic to improve prediction accuracy under noisy, out-of-distribution, and adversarial conditions.

Contribution

The paper presents a novel PLL approach using class activation magnitudes and a new label re-distribution strategy, enhancing robustness against noise and adversarial attacks.

Findings

Improved robustness under high noise levels

Effective handling of out-of-distribution data

Enhanced resistance to adversarial perturbations

Abstract

Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances. Partial-label learning (PLL) is a weakly supervised learning paradigm that allows training classifiers in this context without manual data cleaning. While state-of-the-art methods have good predictive performance, their predictions are sensitive to high noise levels, out-of-distribution data, and adversarial perturbations. We propose a novel PLL method based on subjective logic, which explicitly represents uncertainty by leveraging the magnitudes of the underlying neural network's class activation values. Thereby, we effectively incorporate prior knowledge about the class labels by using a novel label weight re-distribution strategy that we prove to be optimal. We empirically show that our method yields more robust predictions in terms of predictive performance under high PLL noise levels, handling out-of-distribution examples, and handling adversarial perturbations on the test instances.