  A significant limiting factor in training fair classifiers relates to the
presence of dataset bias. In particular, face datasets are typically biased in
terms of attributes such as gender, age, and race. If not mitigated, bias leads
to algorithms that exhibit unfair behaviour towards such groups. In this work,
we address the problem of increasing the diversity of face datasets with
respect to age. Concretely, we propose a novel, generative style-based
architecture for data augmentation that captures fine-grained aging patterns by
conditioning on multi-resolution age-discriminative representations. By
evaluating on several age-annotated datasets in both single- and cross-database
experiments, we show that the proposed method outperforms state-of-the-art
algorithms for age transfer, especially in the case of age groups that lie in
the tails of the label distribution. We further show significantly increased
diversity in the augmented datasets, outperforming all compared methods
according to established metrics.
