Learning under label noise through few-shot human-in-the-loop refinement

Learning under label noise through few-shot human-in-the-loop refinement

Abstract Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is obtaining quality labels. Unlike modalities like video where the videos themselves can be effectively used to...

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Bibliographic Details
Main Authors: Aaqib Saeed, Dimitris Spathis, Jungwoo Oh, Edward Choi, Ali Etemad
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Data-centric machine learning
Robustness
Human-in-the-loop
Model merging
Label noise
Online Access:https://doi.org/10.1038/s41598-025-87046-z
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