  Generative Adversarial Networks (GANs) advance face synthesis through
learning the underlying distribution of observed data. Despite the high-quality
generated faces, some minority groups can be rarely generated from the trained
models due to a biased image generation process. To study the issue, we first
conduct an empirical study on a pre-trained face synthesis model. We observe
that after training the GAN model not only carries the biases in the training
data but also amplifies them to some degree in the image generation process. To
further improve the fairness of image generation, we propose an interpretable
baseline method to balance the output facial attributes without retraining. The
proposed method shifts the interpretable semantic distribution in the latent
space for a more balanced image generation while preserving the sample
diversity. Besides producing more balanced data regarding a particular
attribute (e.g., race, gender, etc.), our method is generalizable to handle
more than one attribute at a time and synthesize samples of fine-grained
subgroups. We further show the positive applicability of the balanced data
sampled from GANs to quantify the biases in other face recognition systems,
like commercial face attribute classifiers and face super-resolution
algorithms.
