EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation?

EigDiff proposes using eigensamples to train diffusion models on much smaller datasets while maintaining performance. By computing latent eigenvectors via PCA in a pre‑trained CLIP space, the method selects a representative training set that includes exemplars close to these eigenvectors and diverse images. Experiments show that EigDiff can reduce training data size by up to 14× and accelerate subset selection by up to 1607× with only marginal performance loss.

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 AI.

• rag
• diffusion