Training neural networks on a large dataset requires substantial computational costs. Dataset reduction selects or synthesizes data instances based on the large dataset, while minimizing the degradation in generalization performance from the full dataset. Existing methods utilize the neural network during the dataset reduction procedure, so the model parameter becomes important factor in preserving the performance after reduction. By depending upon the importance of parameters, this paper introduces a new reduction objective, coined LCMat, which Matches the Loss Curvatures of the original dataset and reduced dataset over the model parameter space, more than the parameter point. This new objective induces a better adaptation of the reduced dataset on the perturbed parameter region than the exact point matching. Particularly, we identify the worst case of the loss curvature gap from the local parameter region, and we derive the implementable upper bound of such worst-case with theoretical analyses. Our experiments on both coreset selection and condensation benchmarks illustrate that LCMat shows better generalization performances than existing baselines.
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
University of Oxford（牛津大学）；Shanghai Jiao Tong University（上海交通大学）；University of Nottingham（诺丁汉大学）；Chinese Academy of Sciences（中国科学院）；University of Edinburgh（爱丁堡大学）
SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（西安交通大学人机混合增强智能全国重点实验室）
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai University（上海大学）
Elucidating the Design Space of Dataset Condensation
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Improving Clinical Dataset Condensation with Mode Connectivity-based Trajectory Surrogates
Institute of Biomedical Engineering, University of Oxford, UK（生物医学工程研究所，牛津大学，英国）；GlaxoSmithKline, London, UK（葛兰素史克，伦敦，英国）；Nuffield Department of Primary Care Health Sciences, University of Oxford, UK（初级保健健康科学尼弗尔部门，牛津大学，英国）；Oxford-Suzhou Institute of Advanced Research (OSCAR), Suzhou, China（牛津-苏州先进研究所（OSCAR），苏州，中国）
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Computer Science, Fudan University（复旦大学计算机学院）
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Washington State University（华盛顿州立大学）；Hanoi Medical University（河内医科大学）；VinUniversity（文森大学）；Hanoi University of Science and Technology（河内科技大学）
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
National University of Singapore（新加坡国立大学）；A*STAR Centre for Frontier AI Research (CFAR)（新加坡科技研究局前沿人工智能研究中心）；Shanghai AI Laboratory（上海人工智能实验室）；University of Nevada, Reno（内华达大学雷诺分校）
Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
Yardi School of Artificial Intelligence, IIT Delhi, India（印度德里理工学院Yardi人工智能学院）；Department of Computer Science and Engineering, IIT Delhi, India（印度德里理工学院计算机科学与工程系）；Department of Chemical Engineering, IIT Delhi, India（印度德里理工学院化学工程系）；Indian Institute of Technology Delhi, Abu Dhabi, Zayed City, Abu Dhabi, UAE（印度德里理工学院阿布扎赫德分校，扎耶德城，阿布扎赫德，阿联酋）
