One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples

TL;DR

This paper introduces OSAMTL-DNLS, an extension of one-step abductive multi-target learning designed to effectively manage complex noisy labels by defining and leveraging diverse noisy label samples.

Contribution

The paper proposes a novel extension of OSAMTL, called OSAMTL-DNLS, specifically aimed at handling complex noisy label samples more effectively.

Findings

Enhanced ability to handle complex noisy labels.

Improved accuracy in multi-target learning scenarios.

New methodology for defining diverse noisy label samples.

Abstract

One-step abductive multi-target learning (OSAMTL) was proposed to handle complex noisy labels. In this paper, giving definition of diverse noisy label samples (DNLS), we propose one-step abductive multi-target learning with DNLS (OSAMTL-DNLS) to expand the methodology of original OSAMTL to better handle complex noisy labels.