The paper proposes a retrieval-based diversity metric (IRS), finds that state-of-the-art diffusion models retrieve at most 77% of training images, and introduces feature-conditioned DiADM to improve unconditional diversity.
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
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Image Generation Diversity Issues and How to Tame Them
The paper proposes a retrieval-based diversity metric (IRS), finds that state-of-the-art diffusion models retrieve at most 77% of training images, and introduces feature-conditioned DiADM to improve unconditional diversity.