One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples
arXiv:2201.07933v1
AI Analysis
This work addresses noisy label handling in machine learning, but appears incremental as it builds upon an existing method.
The authors tackled the problem of handling complex noisy labels by expanding the original OSAMTL methodology to include diverse noisy label samples (DNLS), resulting in the proposed OSAMTL-DNLS method.
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.