Dataset Distillation

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

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Fair Dataset Distillation via Cross-Group Barycenter Alignment
Apr 30, 2026Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei +2Algorithmic Fairness Group Fairness
Western University, London, Ontario · Vector Institute, Toronto, Ontario · Ivey Business School, London, Ontario +1
Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation
Apr 23, 2026Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave +3Healthcare Synthetic Data Generation
Department of Engineering Science University of Oxford, UK · Nuffield Department of Primary Care University of Oxford, UK · GlaxoSmithKline2026 London, UK +1
Rethinking Dataset Distillation: Hard Truths about Soft Labels
Apr 20, 2026Priyam Dey, Aditya Sahdev, Sunny Bhati +2Training Data Selection Dataset Distillation
Vision and AI Lab, Indian Institute of Science, Bangalore · University of Maryland, Baltimore County · IIT Hyderabad
Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
Apr 20, 2026Xiao Lingao, Yang HeDataset Distillation Image Classification
CFAR, Agency for Science, Technology and Research, Singapore 138632 · IHPC, Agency for Science, Technology and Research, Singapore 138632 · National University of Singapore, Singapore 119077
FD $^2$ : A Dedicated Framework for Fine-Grained Dataset Distillation
Mar 26, 2026Hongxu Ma, Guang Li, Shijie Wang +5Fine-Grained Image Classification Dataset Distillation
Zhejiang University · Hokkaido University · The University of Queensland +2
Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks
Mar 16, 2026Yuri Kinoshita, Naoki Nishikawa, Taro ToyoizumiDataset Distillation Statistical Learning Theory
The University of Tokyo, Tokyo, Japan · Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Wako, Japan · RIKEN Center for Advanced Intelligence Project, Nihonbashi, Japan
Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching
Feb 5, 2026Qianxin Xia, Jiawei Du, Yuhan Zhang +6Flow Matching Dataset Distillation
University of Electronic Science and Technology of China, Chengdu, China · CFAR, Singapore · SWJTU, Chengdu, China
Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
Jan 22, 2026Yonghao Xu, Pedram Ghamisi, Qihao WengRemote Sensing Image Understanding Diffusion Model Distillation
Computer Vision and Learning Systems, Department of Electrical Engineering, Linköping University, 581 83 Linköping, Sweden · Helmholtz-Zentrum Dresden-Rossendorf, Responsible AI Group, 09599 Freiberg, Germany · University of Iceland, Faculty of Electrical and Computer Engineering, Reykjavik 102, Iceland +1
A Discrepancy-Based Perspective on Dataset Condensation
Sep 12, 2025Tong Chen, Raghavendra SelvanDistribution Matching Dataset Distillation
Department of Computer Science, University of Copenhagen, Denmark
OD3: Optimization-free Dataset Distillation for Object Detection
Jun 2, 2025Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao +1Dataset Distillation
Mohamed bin Zayed University of Artificial Intelligence · Hong Kong University of Science and Technology (Guangzhou)
Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection
Dec 13, 2024Xinhao Zhong, Shuoyang Sun, Zhaoyang Xu +4Dataset Distillation
Harbin Institute of Technology, Shenzhen · Shanghai Jiao Tong University

Apr 30, 2026, cs.LGJ/K move · Enter open · S save

Fair Dataset Distillation via Cross-Group Barycenter Alignment

Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei, Bissan Ghaddar+1

Western University, London, Ontario · Vector Institute, Toronto, Ontario · Ivey Business School, London, Ontario+1

Algorithmic Fairness Group Fairness Dataset Distillation

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Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit distinct predictive patterns, the distillation process struggles to simultaneously preserve informative signals for all subgroups, regardless of whether group sizes are mildly or severely imbalanced. Consequently, models trained on distilled data can experience substantial performance drops for certain subgroups, leading to fairness gaps. Crucially, these gaps do not disappear by merely correcting group imbalance, since they stem from fundamental mismatches in subgroup predictive patterns rather than from sample-size disparities alone. We therefore formally analyze the interaction between these two sources of bias and cast the solution as identifying a group-imbalance-agnostic barycenter of the predictive information that induces similar representations across all subgroups. By distilling toward this shared aggregate representation, we show that group fairness concerns can be reduced. Our approach is compatible with existing distillation methods, and empirical results show that it substantially reduces bias introduced by dataset distillation. Code is available at https://github.com/mhmoslemi/COBRA.

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