  As applications of generative AI become mainstream, it is important to
understand what generative models are capable of producing, and the extent to
which one can predictably control their outputs. In this paper, we propose a
visualization design, named Concept Lens, for jointly navigating the data
distribution of a generative model, and concept manipulations supported by the
model. Our work is focused on modern vision-based generative adversarial
networks (GAN), and their learned latent spaces, wherein concept discovery has
gained significant interest as a means of image manipulation. Concept Lens is
designed to support users in understanding the diversity of a provided set of
concepts, the relationship between concepts, and the suitability of concepts to
give semantic controls for image generation. Key to our approach is the
hierarchical grouping of concepts, generated images, and the associated joint
exploration. We show how Concept Lens can reveal consistent semantic
manipulations for editing images, while also serving as a diagnostic tool for
studying the limitations and trade-offs of concept discovery methods.
