Xinru Jiang — accepted AI-conference papers

2026

HierAmp: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation

Lin Zhao, Xinru Jiang, Xi Xiao, Qihui Fan, Lei Lu, Yanzhi Wang +4

CVPR 2026

Dataset distillation often prioritizes global semantic proximity when creating small surrogate datasets for original large-scale ones. However, object semantics are inherently hierarchical. For example, the position and appearance of a bird's eyes are constrained by the outline of its head. Global p

Cited by 9 Source ↗code ↗Scholar ↗

2025

Taming Diffusion for Dataset Distillation with High Representativeness

Lin Zhao, Yushu Wu, Xinru Jiang, Jianyang Gu, Yanzhi Wang, Xiaolin Xu +2

ICML 2025poster

Recent deep learning models demand larger datasets, driving the need for dataset distillation to create compact, cost-efficient datasets while maintaining performance. Due to the powerful image generation capability of diffusion, it has been introduced to this field for generating distilled images.…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗