Data-Driven Analysis of Intersectional Bias in Image Classification: A Framework with Bias-Weighted Augmentation

Documenti analoghi

• When Generative Augmentation Hurts: A Benchmark Study of GAN and Diffusion Models for Bias Correction in AI Classification Systems
  di: Gupta, Shesh Narayan, et al.
  Pubblicazione: (2026)
• Decoupling Augmentation Bias in Prompt Learning for Vision-Language Models
  di: Kim, Gahyeon, et al.
  Pubblicazione: (2025)
• FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
  di: Yesmin, Farjana
  Pubblicazione: (2026)
• Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation
  di: Stone, Rebecca S, et al.
  Pubblicazione: (2022)
• Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification
  di: Satriani, Nathanya, et al.
  Pubblicazione: (2026)