Benchmarking the Fairness of Image Upsampling Methods
Mike Laszkiewicz Imant Daunhawer Julia E. Vogt Asja Fischer Johannes Lederer
Recent years have witnessed a rapid development of deep generative models for
creating synthetic media, such as images and videos. While the practical
applications of these models in everyday tasks are enticing, it is crucial to
assess the inherent risks regarding their fairness. In this work, we introduce
a comprehensive framework for benchmarking the performance and fairness of
conditional generative models. We develop a set of
metrics$\unicode{x2013}$inspired by their supervised fairness
counterparts$\unicode{x2013}$to evaluate the models on their fairness and
diversity. Focusing on the specific application of image upsampling, we create
a benchmark covering a wide variety of modern upsampling methods. As part of
the benchmark, we introduce UnfairFace, a subset of FairFace that replicates
the racial distribution of common large-scale face datasets. Our empirical
study highlights the importance of using an unbiased training set and reveals
variations in how the algorithms respond to dataset imbalances. Alarmingly, we
find that none of the considered methods produces statistically fair and
diverse results.