arXiv cs.AI· Andrei Jarca, Vlad Hondru, Florinel-Alin Croitoru, Radu Tudor Ionescu·· 2 天前AI 评分33
EigDiff：用特征样本扩散将训练数据集缩小最多 14 倍
EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation?
AI 导读
EigDiff 通过特征样本（eigensamples）在显著更小的数据集上训练扩散模型，训练数据最多减少 14 倍、子集选择加速最多 1607 倍，性能差异微小。该方法在预训练 CLIP 的潜空间中做 PCA 计算潜特征向量，并以特征向量为参考点挑选由 exemplars 与多样图像组成的代表性训练集。
正文 · 原文
Abstract:Diffusion models are notoriously expensive to train, requiring large-scale datasets to learn how to gradually reverse the noise from images over multiple small denoising steps. Current approaches focus on distilling the number of diffusion steps, neglecting potential redundancies of the training data. In this paper, we explore the potential of harnessing eigensamples to train diffusion models on significantly smaller datasets, while preserving model performance. To mitigate the sensitivity to affine transformations of eigenimages, we harness the latent space of a pre-trained CLIP model. We next compute latent eigenvectors by performing principal component analysis (PCA) in CLIP space. Further, we employ eigenvectors as reference points to guide the selection of a representative training set comprising comprising both exemplars (images whose embeddings are closest to eigenvectors) and diverse images. Our comprehensive experiments with different diffusion models applied on multiple datasets indicate that our approach (EigDiff) can reduce training data by up to 14x and accelerate subset selection by up to 1607x, with marginal performance differences.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20999 [cs.CV] |
| (or arXiv:2610.20999v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20999 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Radu Tudor Ionescu [view email]
[v1]
Wed, 14 Oct 2026 22:36:03 UTC (22,925 KB)
来源：arXiv cs.AI · arxiv.org