Fairness in Facial Attribute Classification with Generative Augmentation

The author proposes a generation-based two-stage framework to train a fair FAC model on biased data without additional annotations, enhancing interpretability and fairness. The method involves detecting spurious attributes via generative models and training a fair model through generative augmentation.