Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models George Stein Jesse C. Cresswell Rasa Hosseinzadeh Yi Sui Brendan Leigh Ross

We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT -L/14 allows for much richer evaluation of generative models.