Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling
Read the original on arXiv Computer Vision →The paper introduces DetNO, a method that enhances diversity in text-to-image diffusion models by maximizing the log-determinant of an RBF Gram matrix over initial noise vectors. DetNO treats diversity as a collective property of the entire batch, using a volume maximization objective that self-attenuates as diversity improves, and includes an anchor-based fidelity term to keep samples realistic. Experiments on SD1.5, SDXL, and SD3 backbones show that DetNO outperforms existing pairwise repulsion methods in intra-batch diversity metrics and yields statistically significant accuracy gains in few-shot classification tasks when used for synthetic data augmentation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.