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For a machine learning model to generalize effectively to unseen data withina particular problem domain, it is well-understood that the data needs to be ofsufficient size and representative of real-world scenarios. Nonetheless,real-world datasets frequently have overrepresented and underrepresentedgroups. One solution to mitigate bias in machine learning is to leverage adiverse and representative dataset. Training a model on a dataset that coversall demographics is crucial to reducing bias in machine learning. However,collecting and labeling large-scale datasets has been challenging, promptingthe use of synthetic data generation and active labeling to decrease the costsof manual labeling. The focus of this study was to generate a robust face imagedataset using the StyleGAN model. In order to achieve a balanced distributionof the dataset among different demographic groups, a synthetic dataset wascreated by controlling the generation process of StyleGaN and annotated fordifferent downstream tasks.

Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Group