Enhancing fairness in deep learning: key tasks, measurement methods, and experimental validation

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Publication Details

Journal
International Journal of Computational Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1504/ijcse.2025.10071366
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhancing fairness in deep learning: key tasks, measurement methods, and experimental validation

Weiyu Shi, Xiaoqian Liu
International Journal of Computational Science and Engineering
Adversarial Robustness in Machine Learning
article

Enhancing fairness in deep learning: key tasks, measurement methods, and experimental validation

Weiyu Shi, Xiaoqian Liu
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Computational Science and EngineeringVol. 1(1)
Openalex Percentile: Top 95%
Adversarial Robustness in Machine Learning
DOISourceOpenAlex
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Enhancing fairness in deep learning: key tasks, measurement methods, and experimental validation — Weiyu Shi, Xiaoqian Liu · International Journal of Computational Science and Engineering (2025) | TGRS Research Map | TGRS