Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing

27 July 2020

Papers citing "Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing"

6 / 6 papers shown

Title
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models Chaomeng Chen Zitong Yu J. Dong Sen Su L. Shen Shutao Xia Xiaochun Cao FedML VLM 143 0 0 03 May 2025
Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training Models Yi Zhang Junyan Wang Jitao Sang 14 27 0 03 Jul 2022
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing Sindhu C. M. Gowda Shalmali Joshi Haoran Zhang Marzyeh Ghassemi CML 24 8 0 27 Aug 2021
Unsupervised Learning of Debiased Representations with Pseudo-Attributes Seonguk Seo Joon-Young Lee Bohyung Han FaML 68 48 0 06 Aug 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,835 0 08 Jul 2016