Method · Diffusion-based synthesis
UniDD
Towards Universal Dataset Distillation via Task-Driven Diffusion
Ding Qi, Jian Li, Junyao Gao, Shuguang Dou, Ying Tai, Jianlong Hu, Bo Zhao, Yabiao Wang, Chengjie Wang, Cairong Zhao
CVPR 2025 · first public 2025-01
In one paragraph
UniDD extends dataset distillation beyond classification to detection and segmentation with a task-driven diffusion model, first mining task-relevant information by training task-specific proxy models (Universal Task Knowledge Mining), then using those proxies to guide a diffusion process that generates task-specific synthetic images with localized, region-level control rather than the global, single-class updates of prior generation methods (Universal Task-Driven Diffusion). The paper reports consistently outperforming state-of-the-art methods on ImageNet-1K, Pascal VOC and MS COCO across classification, detection and segmentation.
Where it sits
- Diffusion-based synthesis (Generative priors)
- Setting: Image classification
- Setting: Detection, segmentation and low-level vision
BibTeX (generated; prefer the venue's official entry)
@article{qi2025towards,
title = {Towards Universal Dataset Distillation via Task-Driven Diffusion},
author = {Ding Qi and Jian Li and Junyao Gao and Shuguang Dou and Ying Tai and Jianlong Hu and Bo Zhao and Yabiao Wang and Chengjie Wang and Cairong Zhao},
journal = {CVPR 2025},
year = {2025}
}Nearby in Diffusion-based synthesis
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