Benchmarking the Fairness of Image Upsampling Methods

Understand

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.