Diffusion-Based Dataset Distillation

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14 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

S $^2$ -RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation
Oct 26, 2026Haoyu Wang, Fei Zhou, Qingqing Qiu +3Dataset Distillation Diffusion-Based Dataset Distillation
Northwestern Polytechnical University · The Chinese University of Hong Kong · Zhejiang University +1
MGPO: Manifold-Guided Diffusion Alignment for Task-Aware Dataset Distillation
Oct 4, 2026Yunyi Chen, Chenru Wang, Xinyi Ye +2Diffusion Model Alignment Dataset Distillation
AGI Lab, Westlake University · Eindhoven University of Technology · Guangdong University of Finance and Economics
Self-Supervised Representation-Guided Generative Dataset Distillation
Aug 4, 2026Mingzhuo Li, Guang Li, Linfeng Ye +4Diffusion Model Guidance Dataset Distillation
Hokkaido University, Japan · University of Toronto, Canada · University of Tokyo, Japan
Dataset Distillation Based on Saliency-Driven Prototype Alignment
Jul 28, 2026Yawen Zou, Wenqi Cai, Guang Li +3Diffusion-Based Dataset Distillation Prototype Learning
University of Toyama · Hokkaido University · University of Fukui
Pool-Select-Refine for Allocation-Aware Generative Dataset Distillation
Jun 1, 2026Wenmin Li, Shunsuke Sakai, Zhongkai Zhao +1Dataset Distillation Knowledge Distillation
Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, 910-8507, Fukui, Japan · College of Computer Science and Artificial Intelligence, Southwest Minzu University, No.168, Wenxing Section, Dajian Road, Konggang Development Zone, Shuangliu District, Chengdu, 610225, Sichuan, China
DIVER: Diving Deeper into Distilled Data via Expressive Semantic Recovery
May 12, 2026Qianxin Xia, Zhiyong Shu, Wenbo Jiang +3Diffusion-Based Dataset Distillation Dataset Distillation
University of Electronic Science and Technology of China, Chengdu, China · Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore · Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR), Singapore.
DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models
May 5, 2026Qichao Wang, Yunhong Lu, Hengyuan Cao +2Diffusion-Based Dataset Distillation Diffusion Model Guidance
Zhejiang University · Shanghai Institute for Advanced Study-Zhejiang University · Shanghai Institute for Mathematics and Interdisciplinary Sciences
Learnability-Guided Diffusion for Dataset Distillation
Apr 1, 2026Jeffrey A. Chan-Santiago, Mubarak ShahDataset Distillation Diffusion-Based Dataset Distillation
Institute of Artificial Intelligence, University of Central Florida
Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment
Mar 30, 2026Tongfei Liu, Yufan Liu, Bing Li +1Dataset Distillation Dataset Condensation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chines Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, Beijing, China. · School of Artificial Intelligence, University of Chinese Academy of Sciences, 1 Yanqihu East Road, Beijing, 101408, Beijing, China. · PeopleAI, Inc., 68 Zhichun Road, Beijing, 100098, Beijing, China. +1
IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation
Mar 14, 2026Chenru Wang, Yunyi Chen, Zijun Yang +2Diffusion-Based Dataset Distillation Dataset Distillation
AGI Lab, Westlake University · Eindhoven University of Technology · Jinan University +2
EVLF: Early Vision-Language Fusion for Generative Dataset Distillation
Mar 8, 2026Wenqi Cai, Yawen Zou, Guang Li +2Diffusion-Based Dataset Distillation Dataset Distillation
University of Toyama · Hokkaido University · University of Fukui
Fixed Anchors Are Not Enough: Dynamic Retrieval and Persistent Homology for Dataset Distillation
Feb 27, 2026Muquan Li, Hang Gou, Yingyi Ma +3Diffusion-Based Dataset Distillation Dataset Distillation
The Laboratory of Intelligent Collaborative Computing of UESTC
ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation
Feb 26, 2026Ayush Roy, Wei-Yang Alex Lee, Rudrasis Chakraborty +1Diffusion-Based Dataset Distillation Dataset Distillation
University at Buffalo, SUNY · Lawrence Livermore National Lab
Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection
Dec 13, 2024Xinhao Zhong, Shuoyang Sun, Zhaoyang Xu +4Diffusion-Based Dataset Distillation Dataset Distillation
Harbin Institute of Technology, Shenzhen · Shanghai Jiao Tong University

Oct 26, 2026J/K move · Enter open · S save

S $^2$ -RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation

Haoyu Wang, Fei Zhou, Qingqing Qiu, Lei Zhang+2

Northwestern Polytechnical University · The Chinese University of Hong Kong · Zhejiang University+1

Dataset Distillation Diffusion-Based Dataset Distillation Semantic Segmentation

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While dataset distillation has witnessed significant advances in image classification, its extension to dense prediction tasks such as semantic segmentation is plagued by two core bottlenecks: bi-level optimization-based approaches suffer from prohibitive computational costs stemming from pixel-wise gradient unrolling, whereas proxy-based generative methods merely infuse visual textures into fixed semantic masks derived from real data, thus failing to condense rich semantic knowledge into a compact set of informative, novel scene generations. To address both bottlenecks simultaneously, we propose a novel Sample-Set Reinforcement Learning (S

^2

-RL) framework for generative semantic segmentation dataset distillation (SSDD). Specifically, S

^2

-RL formulates SSDD as a diffusion model based text-to-image generation paradigm, enabling flexible generation of images with informative, novel semantic distributions via a concise text prompt. Furthermore, we fine-tune a diffusion policy via Group Relative Policy Optimization (GRPO), leveraging a sample-set dual reward paradigm: for individual samples, it enforces semantic alignment with input text prompts and intra-class feature diversity; for sample sets, it maximizes inter-sample semantic distribution diversity by solving a contextual multi-armed bandit problem. These design choices enable S

^2

-RL to distill large-scale semantic segmentation datasets into a compact set of generated samples that encapsulate diverse, informative semantic knowledge without the need of pixel-wise gradient unrolling. Extensive experiments on the ADE20K and COCO datasets demonstrate that S

^2

-RL achieves substantial and consistent improvements over state-of-the-art baselines, establishing a new benchmark for SSDD.