Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is paramount and serves as the foundation for mitigating model biases. However, current methods are limited in accurately pr...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2024-11, p.1-8
Hauptverfasser:	Ma, Yanbiao, Jiao, Licheng, Liu, Fang, Li, Lingling, Ma, Wenping, Yang, Shuyuan, Liu, Xu, Chen, Puhua
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Benchmark testing Computational modeling Correlation Data models Manifolds Maximum likelihood estimation Optimization Predictive models Training
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