Dataset Condensation

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Dataset condensation studies how to replace a large training set with a small set of real or synthetic examples while retaining downstream model performance. Common approaches match gradients, model-training trajectories, representations, or feature distributions; others select representative samples or align distributions with optimal transport. Methods are tested on image benchmarks such as CIFAR and ImageNet, as well as graphs, tabular data, clinical records, and time series. A central challenge is balancing fidelity at very small storage budgets against computation and transfer across models and tasks, while also addressing privacy and vulnerability to poisoned condensed data.

Latest papers

19 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection
Oct 13, 2026Rashi Niyas P, Ritika Jain, Hitika TiwariFall Detection Dataset Condensation
Indian Institute of Technology Madras · Indian Institute of Technology Guwahati
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
Sep 30, 2026Duong M. Nguyen, Trong Nghia Hoang, Hang Thi Nguyen +3Computational Pathology Distribution Matching
University of Illinois Urbana-Champaign · Washington State University · Hanoi Medical University +2
HERALD: High-Fidelity Exemplar Retrieval with Adaptive Landmark Distillation for Heterophily-Aware Graph Condensation
Sep 10, 2026Sujan Chakraborty, Priyanka Saha, Saptarshi BejDataset Condensation Graph Heterophily
School of Data Science, Indian Institute of Science Education and Research Thiruvananthapuram, Thiruvananthapuram, 695551, Kerala, India
Adaptive Latent Trajectory Anchoring for Action Segmentation Dataset Condensation
Jul 10, 2026Artheme Gauthier-Villar, Guodong Ding, Angela YaoTemporal Action Segmentation Dataset Condensation
ETH Zurich, Switzerland · National University of Singapore, Singapore
Condensing Large-Scale Datasets Directly with Minimal Information Loss
Jul 1, 2026Xinyi Shang, Peng Sun, Bei Shi +2Distribution Shift Distribution Matching
University College London, United Kingdom · Zhejiang University, China · Westlake University, China +1
Geometry-Aware Dataset Condensation for Diffusion Model Training
Jun 4, 2026Xiao Cui, Yulei Qin, Mo Zhu +3Dataset Distillation Dataset Condensation
University of Science and Technology of China · Independent Researcher · Zhejiang University +1
Text-attributed Graph Condensation via Text Selection and Attribute Matching
Jun 2, 2026Haowei Han, Yuxiang Wang, Guojia Wan +5Graph Neural Networks Text-Attributed Graph Learning
School of Computer Science Wuhan University Wuhan, China · Institute for Math & AI Wuhan University Wuhan, China
Anchor-guided Hypergraph Condensation with Dual-level Discrimination
May 11, 2026Fan Li, Xiaoyang Wang, Chen Chen +1Hypergraph Neural Networks Dataset Condensation
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia · School of Artificial Intelligence, Shenzhen University, Shenzhen, China.
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 · GlaxoSmithKline London, UK
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
Apr 16, 2026He Yang, Dongyi Lv, Wei Xi +3Federated Learning Aggregation Byzantine-Robust Federated Learning
InkDrop: Invisible Backdoor Attacks Against Dataset Condensation
Mar 30, 2026He Yang, Dongyi Lv, Song Ma +4Backdoor Attacks Dataset Condensation
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
SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation
Mar 29, 2026He Yang, Dongyi Lv, Song Ma +2Backdoor Attacks Dataset Condensation
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China · National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an, China
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
Mar 14, 2026Minh-Duong Nguyen, Thien-Thanh Dao, Le-Tuan Nguyen +2Replay-Based Continual Learning Memory-Efficient Training
College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · Center for AI Research, VinUniversity, Hanoi, Vietnam · Faculty of Information Systems, Phenikaa University, Hanoi, Vietnam +1
Democratising Clinical AI through Dataset Condensation for Classical Clinical Models
Mar 10, 2026Anshul Thakur, Soheila Molaei, Pafue Christy Nganjimi +5Dataset Condensation Clinical Prediction
University of Oxford, Oxford, UK. · Oxford University Hospitals NHS Foundation Trust, Oxford, UK. · GlaxoSmithKline, London, UK +2
Post Training Quantization for Efficient Dataset Condensation
Mar 7, 2026Linh-Tam Tran, Sung-Ho BaeDataset Condensation Post-Training Quantization
Kyung Hee University, Republic of Korea
C $^{2}$ TC: A Training-Free Framework for Efficient Tabular Data Condensation
Feb 25, 2026Sijia Xu, Fan Li, Xiaoyang Wang +2Dataset Condensation Data-Efficient Learning
University of New South Wales, Australia · Shanghai Jiao Tong University, China
A Discrepancy-Based Perspective on Dataset Condensation
Sep 12, 2025Tong Chen, Raghavendra SelvanDistribution Matching Dataset Distillation
Department of Computer Science, University of Copenhagen, Denmark
Beyond Discrete Samples: High Information Density Replay for Efficient Lifelong Person Re-Identification
Aug 3, 2025Mingyu Wang, Haojie Liu, Zhiyong Li +3Replay-Based Continual Learning Dataset Condensation
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

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FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection

Rashi Niyas P, Ritika Jain, Hitika Tiwari

Indian Institute of Technology Madras · Indian Institute of Technology Guwahati

Fall Detection Dataset Condensation Class-Imbalanced Learning Wearable Sensing Synthetic Data Augmentation

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Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Existing methods either rely on hand-crafted features with shallow classifiers, which limits learning from raw signals, or train deep networks on full datasets that require re-sampling to counter class skew and demand substantial data and compute. To address this, we propose a novel Fall detection framework via Normalized-gradient and Embedding matched Synthetic Time-frequency condensation, dubbed FALLNEST, a data-centric approach that distills large, imbalanced IMU datasets into tiny, class-balanced synthetic training sets. Building on dual-domain time-series condensation (CondTSC), FALLNEST introduces a fall-specific 7-channel input with accelerometer vector magnitude, class-balanced K-Means initialization, and normalized gradient matching with gradient clipping, combined with MMD-based embedding alignment and multi-view Fourier-domain augmentation, so that class balance arises from the condensed set itself rather than from re-sampling. We evaluate on SisFall, KFall, and MobiFall using strict subject-wise splits and naturally imbalanced test sets, reporting accuracy, Macro-F1, and PR-AUC over five seeds. A 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows (about only 1% of the training windows) reaches 93.5-95.8% mean accuracy and 0.89-0.98 PR-AUC, with standard deviations of 1.0-1.7% in accuracy, demonstrating the efficacy of our approach.