@techreport{oai:ipsj.ixsq.nii.ac.jp:00209802,
 author = {Koki, Wataoka and Takashi, Matsubara and Kuniaki, Uehara and Koki, Wataoka and Takashi, Matsubara and Kuniaki, Uehara},
 issue = {4},
 month = {Feb},
 note = {Computer vision systems have made significant improvements and been used in a variety of situations. For a practical use, we need to prevent the systems from making unfair decisions for certain individuals. In this sense, the systems have to eliminate the difference between decision makings on the real world and the counterfactual world where users would have different sensitive attributes (e.g., gender and race). In this study, we propose a framework for counterfactual image generation named Causality with Unobserved Variables using Generative Adversarial Networks (CUV-GAN). CUV-GAN can generate counterfactual images as the results of the intervention in the images' attributes and improve the fairness of an image classifier by being trained with generated images as data augmentation., Computer vision systems have made significant improvements and been used in a variety of situations. For a practical use, we need to prevent the systems from making unfair decisions for certain individuals. In this sense, the systems have to eliminate the difference between decision makings on the real world and the counterfactual world where users would have different sensitive attributes (e.g., gender and race). In this study, we propose a framework for counterfactual image generation named Causality with Unobserved Variables using Generative Adversarial Networks (CUV-GAN). CUV-GAN can generate counterfactual images as the results of the intervention in the images' attributes and improve the fairness of an image classifier by being trained with generated images as data augmentation.},
 title = {Counterfactual Image Generation using GAN for Fairness},
 year = {2021}
}