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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11062817/ |
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