A Robust Deep Learning Framework for Mitigating Label Noise With Dual Selective Attention

A Robust Deep Learning Framework for Mitigating Label Noise With Dual Selective Attention

The performance of Machine Learning (ML) models is highly sensitive to data quality, still the impact of label accuracy remains underexplored. In this study, a novel architecture, the Dual Selective Attention Network (DSAN), is proposed to improve robustness against mislabeled data in deep learning...

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Bibliographic Details
Main Authors: Hasnain Hyder, Gulsher Baloch, Amreen Batool, Yong-Woon Kim, Yung-Cheol Byun
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Mislabelled data
data-centric approach
visual geometry group (VGG) 16
dual selective attention network (DSAN)
data quality
ResNet-50
Online Access:https://ieeexplore.ieee.org/document/11062817/
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