Leveraging CLIP for Inferring Sensitive Information and Improving Model
Fairness
Leveraging CLIP for Inferring Sensitive Information and Improving Model
Fairness
Performance disparities across sub-populations are known to exist in deep learning-based vision recognition models, but previous work has largely addressed such fairness concerns assuming knowledge of sensitive attribute labels. To overcome this reliance, previous strategies have involved separate learning structures to expose and adjust for disparities. In this work, we …