Zero-shot racially balanced dataset generation using an existing biased StyleGAN2

Understand

Facial recognition systems have made significant strides thanks to data-heavy deep learning models, but these models rely on large privacy-sensitive datasets.

• Further, many of these datasets lack diversity in terms of ethnicity and demographics, which can lead to biased models that can have serious societal and security implications.
• To address these issues, we propose a methodology that leverages the biased generative model StyleGAN2 to create demographically diverse images of synthetic individuals.
• The synthetic dataset is created using a novel evolutionary search algorithm that targets specific demographic groups.