Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Group
For a machine learning model to generalize effectively to unseen data within
a particular problem domain, it is well-understood that the data needs to be of
sufficient size and representative of real-world scenarios. Nonetheless,
real-world datasets frequently have overrepresented and underrepresented
groups. One solution to mitigate bias in machine learning is to leverage a
diverse and representative dataset. Training a model on a dataset that covers
all demographics is crucial to reducing bias in machine learning. However,
collecting and labeling large-scale datasets has been challenging, prompting
the use of synthetic data generation and active labeling to decrease the costs
of manual labeling. The focus of this study was to generate a robust face image
dataset using the StyleGAN model. In order to achieve a balanced distribution
of the dataset among different demographic groups, a synthetic dataset was
created by controlling the generation process of StyleGaN and annotated for
different downstream tasks.