Noisy with a Chance of Mislabels: A Local and Training Dynamics Perspective on Detecting Label Noise in Deep Classification

Noisy labels are a pervasive challenge in modern supervised learning, especially in highstakes domains such as healthcare, where model reliability is critical. Detecting and mitigating the influence of mislabeled data is essential to improving both performance and interpretability. Building on insights from training dynamics, we propose Local Consistency across Training Epochs (LoCaTE), a class of data-filtering methods that leverages over-parameterized and over-trained neural networks to distinguish clean samples from mislabeled ones. Our approach integrates both local neighborhood information and the behavior of samples across training epochs to identify noise and enhance model robustness. We evaluate our method on real (human) and synthetic label noise across three classification datasets, finding that it achieves competitive F₁ of label error detection and improved downstream accuracy using a lightweight classifier with low added computational cost.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Chentouf, A. Anas

Advisor dc:contributor.advisor

• Ghassemi, Marzyeh

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162504

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162504