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| Date | Paper | Family | Venue |
|---|---|---|---|
| 2026-08 | SRG — Self-Supervised Representation-Guided Generative Dataset Distillation Mingzhuo Li, Guang Li, Linfeng Ye et al.Pre-training & transfer | arXiv 2026 · ↗ | |
| 2026-08 | ProtoBlend — Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending Chongle Ren, Guang Li, Wenbo Huang et al.Video | arXiv 2026 · ↗ | |
| 2026-07 | InfMatch — Dataset Distillation by Influence Matching Haoru Tan, Wang Wang, Sitong Wu et al.Vision–language | CVPR 2026 · ↗ | |
| 2026-07 | Adaptive Latent Trajectory Anchoring for Action Segmentation Dataset Condensation Artheme Gauthier-Villar, Guodong Ding, Angela YaoVideo | ECCV 2026 · ↗ | |
| 2026-07 | CIM — Condensing Large-Scale Datasets Directly with Minimal Information Loss Xinyi Shang, Peng Sun, Bei Shi et al.notable | ECCV 2026 · ↗ | |
| 2026-06 | RAHA — Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation Jongoh Jeong, Sun-Kyung Lee, Kuk-Jin YoonVision–languagenotable | ECCV 2026 · ↗ | |
| 2026-06 | Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space Yue Cao, Jianyang Gu, Vyacheslav Kungurtsev et al.Analysis & theory | ECCV 2026 · ↗ | |
| 2026-06 | DO-ALL — Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation Hyun-Kurl Jang, Jihun Kim, Hyeokjun Kweon et al.notableApplication | ECCV 2026 · ↗ | |
| 2026-06 | Do distilled sets beat coresets? — Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets? Trisha Mittal, Akshay Mehra, Joshua KimballnotableEvaluation & benchmark | arXiv 2026 · ↗ | |
| 2026-06 | GADC — Geometry-Aware Dataset Condensation for Diffusion Model Training Xiao Cui, Yulei Qin, Mo Zhu et al. | ICML 2026 · ↗ | |
| 2026-05 | FOSTER — FOSTER: First-order Dataset Distillation for Text-based Sequential Recommendation Hung Vinh Tran, Tong Chen, Xinyi Gao et al.Other data | arXiv 2026 · ↗ | |
| 2026-05 | D3S2 — D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation Wenjie Zheng, Haoji Hu, Jiali Lu et al.Dense prediction | arXiv 2026 · ↗ | |
| 2026-05 | MDM — Multimodal Distribution Matching for Vision-Language Dataset Distillation Jongoh Jeong, Hoyong Kwon, Minseok Kim et al.Vision–languagenotable | CVPR 2026 · ↗ | |
| 2026-05 | C^2R — Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust? Muquan Li, Yingyi Ma, Yihong Huang et al.notableTrustworthy DD | ICML 2026 · ↗ | |
| 2026-05 | Graph condensation needs a reset — Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence Mridul Gupta, Samyak Jain, Vansh Ramani et al.GraphsnotableSurvey | arXiv 2026 · ↗ | |
| 2026-05 | SubPopMark — From Compression to Accountability: Harmless Copyright Protection for Dataset Distillation Yan Liang, Ziyuan Yang, Mengyu Sun et al.Trustworthy DD | arXiv 2026 · ↗ | |
| 2026-05 | DIVER — DIVER:Diving Deeper into Distilled Data via Expressive Semantic Recovery Qianxin Xia, Zhiyong Shu, Wenbo Jiang et al. | ICML 2026 · ↗ | |
| 2026-05 | CLP-DD — Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models Bincheng Peng, Guang Li, Ping Liu et al.Pre-training & transfer | arXiv 2026 · ↗ | |
| 2026-05 | DMGD — DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models Qichao Wang, Yunhong Lu, Hengyuan Cao et al.notable | CVPR 2026 · ↗ | |
| 2026-04 | COBRA — Fair Dataset Distillation via Cross-Group Barycenter Alignment Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei et al.notableTrustworthy DD | ICML 2026 · ↗ | |
| 2026-04 | BTM — Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave et al.Other dataApplication | arXiv 2026 · ↗ | |
| 2026-04 | Soft Label Pruning and Quantization for Large-Scale Dataset Distillation Xiao Lingao, Yang Henotable | TPAMI 2026 · ↗ | |
| 2026-04 | Hard truths about soft labels — Rethinking Dataset Distillation: Hard Truths about Soft Labels Priyam Dey, Aditya Sahdev, Sunny Bhati et al.notableAnalysis & theory | CVPR 2026 · ↗ | |
| 2026-04 | HoPA — Omnimodal Dataset Distillation via High-order Proxy Alignment Yuxuan Gao, Xiaohao Liu, Xiaobo Xia et al.Audio–visual & omni | arXiv 2026 · ↗ | |
| 2026-04 | Learnability-guided diffusion — Learnability-Guided Diffusion for Dataset Distillation Jeffrey A. Chan-Santiago, Mubarak Shahnotable | CVPR 2026 · ↗ | |
| 2026-03 | Sneakdoor — SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation He Yang, Dongyi Lv, Song Ma et al.notableTrustworthy DD | NeurIPS 2025 · ↗ | |
| 2026-03 | PTM-ST — Multimodal Dataset Distillation via Phased Teacher Models Shengbin Guo, Hang Zhao, Senqiao Yang et al.Vision–languagenotable | ICLR 2026 · ↗ | |
| 2026-03 | FD2 — FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation Hongxu Ma, Guang Li, Shijie Wang et al.notable | ECCV 2026 · ↗ | |
| 2026-03 | DIET — DIET: Learning to Distill Dataset Continually for Recommender Systems Jiaqing Zhang, Hao Wang, Mingjia Yin et al.Other dataApplication | arXiv 2026 · ↗ | |
| 2026-03 | Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks Yuri Kinoshita, Naoki Nishikawa, Taro ToyoizumiAnalysis & theory | ICML 2026 · ↗ | |
| 2026-03 | IMS3 — IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation Chenru Wang, Yunyi Chen, Zijun Yang et al.notable | CVPR 2026 · ↗ | |
| 2026-03 | STemDist — Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression Taehyung Kwon, Yeonje Choi, Yeongho Kim et al.Time series | ICDE 2026 · ↗ | |
| 2026-03 | EVLF — EVLF: Early Vision-Language Fusion for Generative Dataset Distillation Wenqi Cai, Yawen Zou, Guang Li et al.notable | CVPR 2026 · ↗ | |
| 2026-03 | Post Training Quantization for Efficient Dataset Condensation Linh-Tam Tran, Sung-Ho Baenotable | AAAI 2026 · ↗ | |
| 2026-03 | HIERAMP — HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation Lin Zhao, Xinru Jiang, Xi Xiao et al. | CVPR 2026 · ↗ | |
| 2026-03 | VQAE — Vector-Quantized Soft Label Compression for Dataset Distillation Ali Abbasi, Ashkan Shahbazi, Hamed Pirsiavash et al.Text | arXiv 2026 · ↗ | |
| 2026-03 | UniRain — UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization Qianfeng Yang, Qiyuan Guan, Xiang Chen et al.Dense predictionApplication | CVPR 2026 · ↗ | |
| 2026-03 | Harmonic Dataset Distillation for Time Series Forecasting Seungha Hong, Sanghwan Jang, Wonbin Kweon et al.Time seriesnotable | AAAI 2026 · ↗ | |
| 2026-02 | Fixed Anchors Are Not Enough: Dynamic Retrieval and Persistent Homology for Dataset Distillation Muquan Li, Hang Gou, Yingyi Ma et al.notable | CVPR 2026 · ↗ | |
| 2026-02 | ManifoldGD — ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation Ayush Roy, Wei-Yang Alex Lee, Rudrasis Chakraborty et al.notable | CVPR 2026 · ↗ | |
| 2026-02 | C2TC — C$^{2}$TC: A Training-Free Framework for Efficient Tabular Data Condensation Sijia Xu, Fan Li, Xiaoyang Wang et al.Other data | arXiv 2026 · ↗ | |
| 2026-02 | Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression Chenyue Yu, Lingao Xiao, Jinhong Deng et al. | ICLR 2026 · ↗ | |
| 2026-02 | PDS — Multimodal Dataset Distillation Made Simple by Prototype-Guided Data Synthesis Junhyeok Choi, Sangwoo Mo, Minwoo ChaeVision–languagecore | ICLR 2026 · ↗ | |
| 2026-02 | E2D — Accelerating Large-Scale Dataset Distillation via Exploration-Exploitation Optimization Muhammad J. Alahmadi, Peng Gao, Feiyi Wang et al. | arXiv 2026 · ↗ | |
| 2026-02 | DNTK — Efficient Analysis of the Distilled Neural Tangent Kernel Jamie Mahowald, Brian Bell, Alex Ho et al. | arXiv 2026 · ↗ | |
| 2026-02 | ShapeCond — ShapeCond: Fast Shapelet-Guided Dataset Condensation for Time Series Classification Sijia Peng, Yun Xiong, Xi Chen et al.Time series | arXiv 2026 · ↗ | |
| 2026-02 | Path-guided flow matching — Path-Guided Flow Matching for Dataset Distillation Xuhui Li, Zhengquan Luo, Xiwei Liu et al. | arXiv 2026 · ↗ | |
| 2026-01 | Grounding and Enhancing Informativeness and Utility in Dataset Distillation Shaobo Wang, Yantai Yang, Guo Chen et al.notable | ICLR 2026 · ↗ | |
| 2026-01 | DPD — Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion Yonghao Xu, Pedram Ghamisi, Qihao WengApplication | arXiv 2026 · ↗ | |
| 2026-01 | DGS — Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks Mingzhuo Li, Guang Li, Linfeng Ye et al. | arXiv 2026 · ↗ | |
| 2026-01 | CD^2 — CD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning Kexin Bao, Daichi Zhang, Hansong Zhang et al.Application | IJCAI 2025 · ↗ | |
| 2026-01 | Towards Data Quality-Aware Dataset Distillation in Bioimaging Bárbara Capelo, Maria Russo, André Carreiro et al.Evaluation & benchmark | BIOIMAGING 2026 · ↗ | |
| 2026-01 | Attention Hijacking — Attention Hijacking: Backdooring Text Dataset Distillation via Semantic Anchors Hang RenTextTrustworthy DD | ICML 2026 · ↗ | |
| 2026-01 | Asymmetric Synthetic Data Update for Domain Incremental Dataset Distillation Minyoung Oh, SimApplication | ICLR 2026 · ↗ | |
| 2026-01 | UniTSC — One Batch Is Enough: A Unified Dataset Condensation Framework for General Time Series Analysis Wei Shao, Ziquan Fang, Zheqi Lu et al.Time series | ICML 2026 · ↗ | |
| 2026-01 | SCG — Set-Coupled Guidance: Set-Level Coordination in Diffusion-Based Dataset Distillation Ziang Gan, Qi Zhu, Libao Zhang | ICML 2026 · ↗ | |
| 2026-01 | ProtoVAR — ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling Mingyu Wang, Wei Jiang | ICML 2026 · ↗ | |
| 2026-01 | 3DDP — Parameterization-Based Dataset Distillation of 3D Point Clouds through Learnable Shape Morphing Dongwook Kim, Jae-Young YimOther datanotable | ICLR 2026 · ↗ | |
| 2026-01 | OGM — Beyond Soft Label: Dataset Distillation via Orthogonal Gradient Matching Deyu Bo, Xinchao Wangnotable | CVPR 2026 · ↗ | |
| 2026-01 | MMDD — Efficient Multi-modal Dataset Distillation via Analytic Parameter Matching Deyu Bo, Xinchao WangVision–languagenotable | ICML 2026 · ↗ | |
| 2026-01 | Distribution shift in diffusion DD — Mitigating the Distribution Shift of Diffusion-based Dataset Distillation Yue Xu, Chenyu Hu, Pengyu An et al. | CVPR 2026 · ↗ | |
| 2026-01 | BPS — Balanced Dataset Distillation via Modeling Multiple Visual Pattern Distribution Guanghui Shi, Xuefeng Liang, Qixiang WenSelection baseline | CVPR 2026 · ↗ | |
| 2026-01 | AMD — Asynchronous Matching with Dynamic Sampling for Multimodal Dataset Distillation Ding Qi, Jian Li, Shuguang Dou et al.Vision–languagenotable | ICLR 2026 · ↗ | |
| 2025-12 | Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation Yiming Qian, Thorsten Neumann, Xueyining Huang et al.Other dataApplication | ICAIFW 2025 · ↗ | |
| 2025-12 | HALD — Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift Jiacheng Cui, Bingkui Tong, Xinyue Bi et al.notable | ICML 2026 · ↗ | |
| 2025-12 | SFVD — Distill Video Datasets into Images Zhenghao Zhao, Haoxuan Wang, Kai Wang et al.Video | arXiv 2025 · ↗ | |
| 2025-12 | DiRe — DiRe: Diversity-promoting Regularization for Dataset Condensation Saumyaranjan Mohanty, Aravind Reddy, Konda Reddy Mopuri | WACV 2026 · ↗ | |
| 2025-12 | EEG-DLite — EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training Yuting Tang, Weibang Jiang, Shanglin Li et al.Other data | AAAI 2026 · ↗ | |
| 2025-12 | GeoDM — GeoDM: Geometry-aware Distribution Matching for Dataset Distillation Xuhui Li, Zhengquan Luo, Zihui Cui et al. | ICML 2026 · ↗ | |
| 2025-12 | Utility boundary laws — Utility Boundary of Dataset Distillation: Scaling and Configuration-Coverage Laws Zhengquan Luo, Zhiqiang XuAnalysis & theory | ICML 2026 · ↗ | |
| 2025-12 | Text dataset distillation report — Technical Report on Text Dataset Distillation Keith Ando Ogawa, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara et al.TextnotableSurvey | arXiv 2025 · ↗ | |
| 2025-12 | CoDA — CoDA: From Text-to-Image Diffusion Models to Training-Free Dataset Distillation Letian Zhou, Songhua Liu, Xinchao Wangnotable | ICLR 2026 · ↗ | |
| 2025-12 | TGDD — TGDD: Trajectory Guided Dataset Distillation with Balanced Distribution Fengli Ran, Xiao Pu, Bo Liu et al. | AAAI 2026 · ↗ | |
| 2025-11 | Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation Xiao Cui, Yulei Qin, Wengang Zhou et al.notable | NeurIPS 2025 · ↗ | |
| 2025-11 | Algorithmic Guarantees for Distilling Supervised and Offline RL Datasets Aaryan Gupta, Rishi Saket, Aravindan RaghuveerOther datanotable | ICLR 2026 · ↗ | |
| 2025-11 | SR-GM — Decoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensation Lian Shen, Zhendan Chen, Meijia Song et al.Graphs | arXiv 2025 · ↗ | |
| 2025-11 | RLDD — Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling Xiao Cui, Yulei Qin, Xinyue Li et al.notable | AAAI 2026 · ↗ | |
| 2025-11 | ADSA — Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation Chenyang Jiang, Hang Zhao, Xinyu Zhang et al.notable | NeurIPS 2025 · ↗ | |
| 2025-11 | DAVDD — Decoupled Audio-Visual Dataset Distillation Wenyuan Li, Guang Li, Keisuke Maeda et al.Audio–visual & omni | arXiv 2025 · ↗ | |
| 2025-11 | Linear Gradient Matching — Dataset Distillation for Pre-Trained Self-Supervised Vision Models George Cazenavette, Antonio Torralba, Vincent SitzmannPre-training & transfernotable | NeurIPS 2025 · ↗ | |
| 2025-11 | DDTime — DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting Yuqi Li, Kuiye Ding, Chuanguang Yang et al.Time series | arXiv 2025 · ↗ | |
| 2025-11 | Low-Level Dataset Distillation for Medical Image Enhancement Fengzhi Xu, Ziyuan Yang, Mengyu Sun et al.Dense prediction | arXiv 2025 · ↗ | |
| 2025-11 | PACE — Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillation Shuhan Ye, Yi Yu, Qixin Zhang et al.Other data | arXiv 2025 · ↗ | |
| 2025-11 | PRISM — PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors Brian B. Moser, Shalini Sarode, Federico Raue et al. | TMLR 2026 · ↗ | |
| 2025-11 | DP-GENG — DP-GENG: Differentially Private Dataset Distillation Guided by DP-Generated Data Shuo Shi, Jinghuai Zhang, Shijie Jiang et al.notableTrustworthy DD | AAAI 2026 · ↗ | |
| 2025-11 | ImageBindDC — ImagebindDC: Compressing Multi-modal Data with Imagebind-based Condensation Yue Min, Shaobo Wang, Jiaze Li et al.Audio–visual & omninotable | AAAI 2026 · ↗ | |
| 2025-10 | CovMatch — CovMatch: Cross-Covariance Guided Multimodal Dataset Distillation with Trainable Text Encoder Yongmin Lee, Hye Won ChungVision–languagenotable | NeurIPS 2025 · ↗ | |
| 2025-10 | DAP — Diffusion Models as Dataset Distillation Priors Duo Su, Huyu Wu, Huanran Chen et al.notable | ICLR 2026 · ↗ | |
| 2025-10 | Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation Muquan Li, Hang Gou, Dongyang Zhang et al. | NeurIPS 2025 · ↗ | |
| 2025-10 | DEDA — Diversity-Enhanced Distribution Alignment for Dataset Distillation Hongcheng Li, Yucan Zhou, Xiaoyan Gu et al. | ICCV 2025 · ↗ | |
| 2025-09 | GSDD — Parameterizing Dataset Distillation via Gaussian Splatting Chenyang Jiang, Zhengcen Li, Hang Zhao et al. | arXiv 2025 · ↗ | |
| 2025-09 | HoP-TM — High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation Le Dong, Jinghao Bian, Jingyang Hou et al.Application | MICCAI 2025 · ↗ | |
| 2025-09 | RD3 — Rectified Decoupled Dataset Distillation: A Closer Look for Fair and Comprehensive Evaluation Xinhao Zhong, Shuoyang Sun, Xulin Gu et al.notableEvaluation & benchmark | ICLR 2026 · ↗ | |
| 2025-09 | EDGE — Efficient Multimodal Dataset Distillation via Generative Models Zhenghao Zhao, Haoxuan Wang, Junyi Wu et al.Vision–languagecore | NeurIPS 2025 · ↗ | |
| 2025-09 | A Discrepancy-Based Perspective on Dataset Condensation Tong Chen, Raghavendra SelvanAnalysis & theory | arXiv 2025 · ↗ | |
| 2025-08 | Distilling Reinforcement Learning into Single-Batch Datasets Connor Wilhelm, Dan VenturaOther dataApplication | ECAI 2025 · ↗ | |
| 2025-08 | Dosser — Improving Noise Efficiency in Privacy-preserving Dataset Distillation Runkai Zheng, Vishnu Asutosh Dasu, Yinong Oliver Wang et al.notableTrustworthy DD | ICCV 2025 · ↗ | |
| 2025-08 | Enhancing Diffusion-based Dataset Distillation via Adversary-Guided Curriculum Sampling Lexiao Zou, Gongwei Chen, Yanda Chen et al. | ICME 2025 · ↗ | |
| 2025-08 | Dataset Condensation with Color Compensation Huyu Wu, Duo Su, Junjie Hou et al. | TMLR 2025 · ↗ | |
| 2025-07 | Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation Sheng-Feng Yu, Jia-Jiun Yao, Wei-Chen ChiuPre-training & transfer | ICLR 2025 · ↗ | |
| 2025-07 | Rate-utility DD — Dataset Distillation as Data Compression: A Rate-Utility Perspective Youneng Bao, Yiping Liu, Zhuo Chen et al.notable | ICCV 2025 · ↗ | |
| 2025-07 | Label-Consistent Dataset Distillation with Detector-Guided Refinement Yawen Zou, Guang Li, Zi Wang et al. | arXiv 2025 · ↗ | |
| 2025-07 | D2C — Accelerating Diffusion Model Training under Minimal Budgets: A Condensation-Based Perspective Rui Huang, Shitong Shao, Zikai Zhou et al.Pre-training & transfernotableApplication | CVPR 2026 · ↗ | |
| 2025-07 | Information-Guided Diffusion Sampling for Dataset Distillation Linfeng Ye, Shayan Mohajer Hamidi, Guang Li et al. | NeurIPS 2025 Workshop · ↗ | |
| 2025-07 | Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling Mingzhuo Li, Guang Li, Jiafeng Mao et al. | ICCV 2025 Workshop · ↗ | |
| 2025-06 | VLCP — Dataset Distillation via Vision-Language Category Prototype Yawen Zou, Guang Li, Duo Su et al.notable | ICCV 2025 · ↗ | |
| 2025-06 | FADRM — FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Jiacheng Cui, Xinyue Bi, Yaxin Luo et al.notable | NeurIPS 2025 · ↗ | |
| 2025-06 | CaO2 — CaO$_2$: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation Haoxuan Wang, Zhenghao Zhao, Junyi Wu et al.notable | ICCV 2025 · ↗ | |
| 2025-06 | FedWSIDD — FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation Haolong Jin, Shenglin Liu, Cong Cong et al.Application | MICCAI 2025 · ↗ | |
| 2025-06 | Soft labels leak — Dataset distillation for memorized data: Soft labels can leak held-out teacher knowledge Freya Behrens, Lenka ZdeborováTrustworthy DD | ICLR 2026 · ↗ | |
| 2025-06 | Flowing Datasets with Wasserstein over Wasserstein Gradient Flows Clément Bonet, Christophe Vauthier, Anna KorbaAnalysis & theory | ICML 2025 · ↗ | |
| 2025-06 | OD3 — OD3: Optimization-free Dataset Distillation for Object Detection Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao et al.Dense predictionnotable | ICLR 2026 · ↗ | |
| 2025-06 | OPTICAL — OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation Xiao Cui, Yulei Qin, Wengang Zhou et al. | CVPR 2025 · ↗ | |
| 2025-05 | HDD — Hyperbolic Dataset Distillation Wenyuan Li, Guang Li, Keisuke Maeda et al.notable | NeurIPS 2025 · ↗ | |
| 2025-05 | PRISM — PRISM: Video Dataset Condensation with Progressive Refinement and Insertion for Sparse Motion Jaehyun Choi, Jiwan Hur, Gyojin Han et al.Videonotable | CVPR 2026 · ↗ | |
| 2025-05 | DAViD — Dynamic-Aware Video Distillation: Optimizing Temporal Resolution Based on Video Semantics Yinjie Zhao, Heng Zhao, Bihan Wen et al.Video | arXiv 2025 · ↗ | |
| 2025-05 | Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets Xulin Gu, Xinhao Zhong, Zhixing Wei et al.Video | arXiv 2025 · ↗ | |
| 2025-05 | Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory Mingzhuo Li, Guang Li, Jiafeng Mao et al. | ICIP 2025 · ↗ | |
| 2025-05 | Data-Distill-Net — Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning Wenyang Liao, Quanziang Wang, Yichen Wu et al.Application | arXiv 2025 · ↗ | |
| 2025-05 | MGD³ — MGD$^3$: Mode-Guided Dataset Distillation using Diffusion Models Jeffrey A. Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak et al.notable | ICML 2025 · ↗ | |
| 2025-05 | D3HR — Taming Diffusion for Dataset Distillation with High Representativeness Lin Zhao, Yushu Wu, Xinru Jiang et al.notable | ICML 2025 · ↗ | |
| 2025-05 | CONCORD — CONCORD: Concept-Informed Diffusion for Dataset Distillation Jianyang Gu, Haonan Wang, Ruoxi Jia et al. | arXiv 2025 · ↗ | |
| 2025-05 | DD-Ranking — DD-Ranking: Rethinking the Evaluation of Dataset Distillation Zekai Li, Xinhao Zhong, Samir Khaki et al.coreEvaluation & benchmark | arXiv 2025 · ↗ | |
| 2025-05 | RepBlend — Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation Xin Zhang, Ziruo Zhang, Jiawei Du et al.Vision–languagenotable | NeurIPS 2025 · ↗ | |
| 2025-04 | UniDetox — UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation Huimin Lu, Masaru Isonuma, Junichiro Mori et al.TextnotableTrustworthy DD | ICLR 2025 · ↗ | |
| 2025-04 | Latent Video Dataset Distillation Ning Li, Antai Andy Liu, Jingran Zhang et al.Video | CVPR 2025 Workshop · ↗ | |
| 2025-04 | Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions Luyang Fang, Xiaowei Yu, Jiazhang Cai et al.TextSurvey | arXiv 2025 · ↗ | |
| 2025-04 | GPS — GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning Mingchuan Ma, Yuhao Zhou, Jindi Lv et al.Application | arXiv 2025 · ↗ | |
| 2025-03 | SADM — Dataset Distillation of 3D Point Clouds via Distribution Matching Jae-Young Yim, Dongwook Kim, Jae-Young SimOther datanotable | NeurIPS 2025 · ↗ | |
| 2025-03 | NRR-DD — Enhancing Dataset Distillation via Non-Critical Region Refinement Minh-Tuan Tran, Trung Le, Xuan-May Le et al. | CVPR 2025 · ↗ | |
| 2025-03 | CCFS — Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation Yanda Chen, Gongwei Chen, Miao Zhang et al. | CVPR 2025 · ↗ | |
| 2025-03 | Condensing Action Segmentation Datasets via Generative Network Inversion Guodong Ding, Rongyu Chen, Angela YaoVideonotable | CVPR 2025 · ↗ | |
| 2025-03 | DDiF — Distilling Dataset into Neural Field Donghyeok Shin, HeeSun Bae, Gyuwon Sim et al.core | ICLR 2025 · ↗ | |
| 2025-03 | Spectral filtering view — Understanding Dataset Distillation via Spectral Filtering Deyu Bo, Songhua Liu, Xinchao WangAnalysis & theory | ICLR 2026 · ↗ | |
| 2025-02 | NCFM — Dataset Distillation with Neural Characteristic Function: A Minmax Perspective Shaobo Wang, Yicun Yang, Zhiyuan Liu et al.notable | CVPR 2025 · ↗ | |
| 2025-02 | GRADMM — Synthetic Text Generation for Training Large Language Models via Gradient Matching Dang Nguyen, Zeman Li, Mohammadhossein Bateni et al.Textnotable | ICML 2025 · ↗ | |
| 2025-02 | Liu & Du survey — The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Ping Liu, Jiawei DucoreSurvey | arXiv 2025 · ↗ | |
| 2025-02 | Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data Ziyuan Yang, Ming Yan, Yi Zhang et al.notableTrustworthy DD | AAAI 2026 · ↗ | |
| 2025-02 | TD3 — TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation Jiaqing Zhang, Mingjia Yin, Hao Wang et al.Other data | WWW 2025 · ↗ | |
| 2025-01 | TDColER — On Learning Representations for Tabular Data Distillation Inwon Kang, Parikshit Ram, Yi Zhou et al.Other data | arXiv 2025 · ↗ | |
| 2025-01 | Pushforward quantization — Dataset Distillation as Pushforward Optimal Quantization Hong Ye Tan, Emma Sladenotable | ICLR 2026 · ↗ | |
| 2025-01 | CV-DD — Dataset Distillation via Committee Voting Jiacheng Cui, Zhaoyi Li, Xiaochen Ma et al. | arXiv 2025 · ↗ | |
| 2025-01 | FocusDD — FocusDD: Real-World Scene Infusion for Robust Dataset Distillation Youbing Hu, Yun Cheng, Olga Saukh et al. | arXiv 2025 · ↗ | |
| 2025-01 | Generative Dataset Distillation Based on Self-knowledge Distillation Longzhen Li, Guang Li, Ren Togo et al. | ICASSP 2025 · ↗ | |
| 2025-01 | ROME — ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck Zheng ZhounotableTrustworthy DD | ICML 2025 · ↗ | |
| 2025-01 | RDC — Robust Dataset Condensation using Supervised Contrastive Learning Nicole Hee-Yeon KimnotableTrustworthy DD | ICCV 2025 · ↗ | |
| 2025-01 | UniDD — Towards Universal Dataset Distillation via Task-Driven Diffusion Ding Qi, Jian Li, Junyao Gao et al.Dense predictionnotable | CVPR 2025 · ↗ | |
| 2025-01 | Shang et al. survey — Dataset Distillation in the Era of Large-Scale Data: Methods, Analysis, and Future Directions Xinyi ShangVision–languageSurvey | arXiv 2025 · ↗ | |
| 2025-01 | Point Cloud DD — Point Cloud Dataset Distillation Deyu Bo, Xinchao WangOther datanotable | ICML 2025 · ↗ | |
| 2025-01 | IGD — Influence-Guided Diffusion for Dataset Distillation Mingyang Chen, Jiawei Du, Bo Huang et al.notable | ICLR 2025 · ↗ | |
| 2025-01 | CondenseLM — CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching Cheng Shen, Yew-Soon Ong, Joey Tianyi ZhouText | EMNLP 2025 · ↗ | |
| 2024-12 | Video DC study — A Large-Scale Study on Video Action Dataset Condensation Yang Chen, Sheng Guo, Bo Zheng et al.VideoEvaluation & benchmark | arXiv 2024 · ↗ | |
| 2024-12 | FedVCK — FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis Guochen Yan, Luyuan Xie, Xinyi Gao et al.notableApplication | AAAI 2025 · ↗ | |
| 2024-12 | Adaptive Dataset Quantization Muquan Li, Dongyang Zhang, Qiang Dong et al.notable | AAAI 2025 · ↗ | |
| 2024-12 | CMI — Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information Xinhao Zhong, Bin Chen, Hao Fang et al. | ICLR 2025 · ↗ | |
| 2024-12 | DDM — Decomposed Distribution Matching in Dataset Condensation Sahar Rahimi Malakshan, Mohammad Saeed Ebrahimi Saadabadi, Ali Dabouei et al.notable | WACV 2025 · ↗ | |
| 2024-12 | Provable KRR DD — Provable and Efficient Dataset Distillation for Kernel Ridge Regression Yilan Chen, Wei Huang, Tsui-Wei Wengnotable | NeurIPS 2024 · ↗ | |
| 2024-11 | FairDD — FairDD: Fair Dataset Distillation Qihang Zhou, Shenhao Fang, Shibo He et al.notableTrustworthy DD | NeurIPS 2025 · ↗ | |
| 2024-11 | DELT — DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation Zhiqiang Shen, Ammar Sherif, Zeyuan Yin et al.notable | CVPR 2025 · ↗ | |
| 2024-11 | IDTD — Video Set Distillation: Information Diversification and Temporal Densification Yinjie Zhao, Heng Zhao, Bihan Wen et al.Video | arXiv 2024 · ↗ | |
| 2024-11 | DD_LNL — Dataset Distillers Are Good Label Denoisers In the Wild Lechao Cheng, Kaifeng Chen, Jiyang Li et al.Trustworthy DD | arXiv 2024 · ↗ | |
| 2024-11 | AutoPalette — Color-Oriented Redundancy Reduction in Dataset Distillation Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh et al. | NeurIPS 2024 · ↗ | |
| 2024-11 | BEARD — BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation Zheng Zhou, Wenquan Feng, Shuchang Lyu et al.Evaluation & benchmark | arXiv 2024 · ↗ | |
| 2024-10 | Offline Behavior Distillation Shiye Lei, Sen Zhang, Dacheng TaoOther datanotableApplication | NeurIPS 2024 · ↗ | |
| 2024-10 | TimeDC — Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching--Extended Version Hao Miao, Ziqiao Liu, Yan Zhao et al.Time series | VLDB 2025 · ↗ | |
| 2024-10 | EDF — Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios Kai Wang, Zekai Li, Zhi-Qi Cheng et al. | CVPR 2025 · ↗ | |
| 2024-10 | LPLD — Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation? Lingao Xiao, Yang Henotable | NeurIPS 2024 · ↗ | |
| 2024-10 | Teddy — Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching Ruonan Yu, Songhua Liu, Jingwen Ye et al. | ECCV 2024 · ↗ | |
| 2024-10 | MKDT — Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks Siddharth Joshi, Jiayi Ni, Baharan MirzasoleimanPre-training & transfernotable | ICLR 2025 · ↗ | |
| 2024-10 | DRUPI — DRUPI: Dataset Reduction Using Privileged Information Shaobo Wang, Youxin Jiang, Tianle Niu et al. | arXiv 2024 · ↗ | |
| 2024-10 | DSDM — Diversified Semantic Distribution Matching for Dataset Distillation Hongcheng Li, Yucan Zhou, Xiaoyan Gu et al.Other data | MM 2024 · ↗ | |
| 2024-09 | DWA — Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment Jiawei Du, Xin Zhang, Juncheng Hu et al.notable | NeurIPS 2024 · ↗ | |
| 2024-09 | HFLDD — Dataset Distillation-based Hybrid Federated Learning on Non-IID Data Xiufang Shi, Wei Zhang, Yuheng Li et al.Application | IEEE TNSE 2026 · ↗ | |
| 2024-09 | Label-Augmented Dataset Distillation Seoungyoon Kang, Youngsun Lim, Hyunjung Shim | WACV 2025 · ↗ | |
| 2024-09 | Towards Model-Agnostic Dataset Condensation by Heterogeneous Models Jun-Yeong Moon, Jung Uk Kim, Gyeong-Moon Park | ECCV 2024 · ↗ | |
| 2024-09 | Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu et al.Analysis & theory | arXiv 2024 · ↗ | |
| 2024-08 | UDD — UDD: Dataset Distillation via Mining Underutilized Regions Shiguang Wang, Zhongyu Zhang, Jian Cheng | PRCV 2024 · ↗ | |
| 2024-08 | NSD — Neural Spectral Decomposition for Dataset Distillation Shaolei Yang, Shen Cheng, Mingbo Hong et al. | ECCV 2024 · ↗ | |
| 2024-08 | LTDD — Distilling Long-tailed Datasets Zhenghao Zhao, Haoxuan Wang, Yuzhang Shang et al.notable | CVPR 2025 · ↗ | |
| 2024-08 | Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation Shaobo Wang, Yantai Yang, Qilong Wang et al. | CVPR 2025 Workshop · ↗ | |
| 2024-08 | Histo-DD — Dataset Distillation for Histopathology Image Classification Cong Cong, Shiyu Xuan, Sidong Liu et al.Application | arXiv 2024 · ↗ | |
| 2024-08 | Generative Dataset Distillation Based on Diffusion Model Duo Su, Junjie Hou, Guang Li et al. | ECCV 2024 Workshop · ↗ | |
| 2024-08 | HeLlO — Heavy Labels Out! Dataset Distillation with Label Space Lightening Ruonan Yu, Songhua Liu, Zigeng Chen et al.notable | ICCV 2025 · ↗ | |
| 2024-08 | INFER — Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator Xin Zhang, Jiawei Du, Ping Liu et al.notable | ICLR 2025 · ↗ | |
| 2024-08 | PAD — Prioritize Alignment in Dataset Distillation Zekai Li, Ziyao Guo, Wangbo Zhao et al.notable | arXiv 2024 · ↗ | |
| 2024-08 | CollabDM — One-Shot Collaborative Data Distillation William Holland, Chandra Thapa, Sarah Ali Siddiqui et al.Application | ECAI 2024 · ↗ | |
| 2024-07 | Dataset Distillation for Offline Reinforcement Learning Jonathan Light, Yuanzhe Liu, Ziniu HuOther dataApplication | ICML 2024 Workshop · ↗ | |
| 2024-07 | D4M — D$^4$M: Dataset Distillation via Disentangled Diffusion Model Duo Su, Junjie Hou, Weizhi Gao et al.landmark | CVPR 2024 · ↗ | |
| 2024-07 | Dataset Distillation in Medical Imaging: A Feasibility Study Muyang Li, Can Cui, Quan Liu et al.Evaluation & benchmark | arXiv 2024 · ↗ | |
| 2024-07 | ATT — Dataset Distillation by Automatic Training Trajectories Dai Liu, Jindong Gu, Hu Cao et al. | ECCV 2024 · ↗ | |
| 2024-07 | DDFAD — DDFAD: Dataset Distillation Framework for Audio Data Wenbo Jiang, Rui Zhang, Hongwei Li et al.Other data | arXiv 2024 · ↗ | |
| 2024-07 | FYI — FYI: Flip Your Images for Dataset Distillation Byunggwan Son, Youngmin Oh, Donghyeon Baek et al. | ECCV 2024 · ↗ | |
| 2024-07 | Dataset Quantization with Active Learning based Adaptive Sampling Zhenghao Zhao, Yuzhang Shang, Junyi Wu et al. | ECCV 2024 · ↗ | |
| 2024-07 | D3S — Large Scale Dataset Distillation with Domain Shift Noel Loo, Alaa Maalouf, Ramin Hasani et al. | ICML 2024 · ↗ | |
| 2024-06 | GC-Bench — GC-Bench: An Open and Unified Benchmark for Graph Condensation Qingyun Sun, Ziying Chen, Beining Yang et al.GraphsnotableEvaluation & benchmark | NeurIPS 2024 · ↗ | |
| 2024-06 | MCT — Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory Wenliang Zhong, Haoyu Tang, Qinghai Zheng et al. | CVPR 2025 · ↗ | |
| 2024-06 | GC4NC — GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights Shengbo Gong, Juntong Ni, Noveen Sachdeva et al.GraphsEvaluation & benchmark | NeurIPS 2025 Datasets and Benchmarks Track · ↗ | |
| 2024-06 | Behaviour Distillation Andrei Lupu, Chris Lu, Jarek Liesen et al.Other dataApplication | ICLR 2024 · ↗ | |
| 2024-06 | InfoDist — Image Distillation for Safe Data Sharing in Histopathology Zhe Li, Bernhard KainzApplication | MICCAI 2024 · ↗ | |
| 2024-06 | A label is worth a thousand images — A Label is Worth a Thousand Images in Dataset Distillation Tian Qin, Zhiwei Deng, David Alvarez-MelislandmarkAnalysis & theory | NeurIPS 2024 · ↗ | |
| 2024-06 | H-GLaD — Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation Xinhao Zhong, Hao Fang, Bin Chen et al.notable | CVPR 2025 · ↗ | |
| 2024-06 | What is DD learning? — What is Dataset Distillation Learning? William Yang, Ye Zhu, Zhiwei Deng et al.notableAnalysis & theory | ICML 2024 · ↗ | |
| 2024-06 | LoRS — Low-Rank Similarity Mining for Multimodal Dataset Distillation Yue Xu, Zhilin Lin, Yusong Qiu et al.Vision–languagecore | ICML 2024 · ↗ | |
| 2024-06 | Bias in DD — Mitigating Bias in Dataset Distillation Justin Cui, Ruochen Wang, Yuanhao Xiong et al.Trustworthy DD | ICML 2024 · ↗ | |
| 2024-06 | Dataset-Distillation Generative Model for Speech Emotion Recognition Fabian Ritter-Gutierrez, Kuan-Po Huang, Jeremy H. M Wong et al.Other data | Interspeech 2024 · ↗ | |
| 2024-06 | CondTSF — CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting Jianrong Ding, Zhanyu Liu, Guanjie Zheng et al.Time series | NeurIPS 2024 · ↗ | |
| 2024-06 | DANCE — DANCE: Dual-View Distribution Alignment for Dataset Condensation Hansong Zhang, Shikun Li, Fanzhao Lin et al. | IJCAI 2024 · ↗ | |
| 2024-06 | BACON — BACON: Bayesian Optimal Condensation Framework for Dataset Distillation Zheng Zhou, Hongbo Zhao, Guangliang Cheng et al. | arXiv 2024 · ↗ | |
| 2024-06 | Adaptive Backdoor Attacks Against Dataset Distillation for Federated Learning Ze Chai, Zhipeng Gao, Yijing Lin et al.Trustworthy DD | ICC 2024 · ↗ | |
| 2024-06 | LQM — Dataset Condensation with Latent Quantile Matching Wei Wei, Tom De Schepper, Kevin MetsGraphs | CVPR 2024 Workshop · ↗ | |
| 2024-05 | SelMatch — SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching Yongmin Lee, Hye Won Chungnotable | ICML 2024 · ↗ | |
| 2024-05 | HCDC — Calibrated Dataset Condensation for Faster Hyperparameter Search Mucong Ding, Yuancheng Xu, Tahseen Rabbani et al.Application | arXiv 2024 · ↗ | |
| 2024-05 | ReLA — Efficiency for Free: Ideal Data Are Transportable Representations Peng Sun, Yi Jiang, Tao LinPre-training & transfernotable | NeurIPS 2024 · ↗ | |
| 2024-05 | GIFT — GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost Xinyi Shang, Peng Sun, Tao Linnotable | ICLR 2025 · ↗ | |
| 2024-05 | GCondenser — GCondenser: Benchmarking Graph Condensation Yilun Liu, Ruihong Qiu, Zi HuangGraphsEvaluation & benchmark | arXiv 2024 · ↗ | |
| 2024-05 | DeSA — Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors Chun-Yin Huang, Kartik Srinivas, Xin Zhang et al.notableApplication | ICML 2024 · ↗ | |
| 2024-05 | Curriculum Dataset Distillation Zhiheng Ma, Anjia Cao, Funing Yang et al. | TIP 2025 · ↗ | |
| 2024-04 | FedAF — An Aggregation-Free Federated Learning for Tackling Data Heterogeneity Yuan Wang, Huazhu Fu, Renuga Kanagavelu et al.notableApplication | CVPR 2024 · ↗ | |
| 2024-04 | Generative Dataset Distillation: Balancing Global Structure and Local Details Longzhen Li, Guang Li, Ren Togo et al. | CVPR 2024 Workshop · ↗ | |
| 2024-04 | Distilled Datamodel with Reverse Gradient Matching Jingwen Ye, Ruonan Yu, Songhua Liu et al.notableApplication | CVPR 2024 · ↗ | |
| 2024-04 | EDC — Elucidating the Design Space of Dataset Condensation Shitong Shao, Zikai Zhou, Huanran Chen et al.core | NeurIPS 2024 · ↗ | |
| 2024-04 | SC-DD — Self-supervised Dataset Distillation: A Good Compression Is All You Need Muxin Zhou, Zeyuan Yin, Shitong Shao et al.Pre-training & transfer | arXiv 2024 · ↗ | |
| 2024-03 | IID — Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation Wenxiao Deng, Wenbin Li, Tianyu Ding et al. | CVPR 2024 · ↗ | |
| 2024-03 | DiLM — DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi et al.Text | NAACL 2024 · ↗ | |
| 2024-03 | Progressive trajectory matching for medical dataset distillation Zhen Yu, Yang Liu, Qingchao ChenApplication | arXiv 2024 · ↗ | |
| 2024-03 | DD-RobustBench — DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation Yifan Wu, Jiawei Du, Ping Liu et al.Evaluation & benchmark | TIP 2025 · ↗ | |
| 2024-03 | GUARD — Towards Adversarially Robust Dataset Distillation by Curvature Regularization Eric Xue, Yijiang Li, Haoyang Liu et al.notableTrustworthy DD | AAAI 2025 · ↗ | |
| 2024-03 | CondTSC — Dataset Condensation for Time Series Classification via Dual Domain Matching Zhanyu Liu, Ke Hao, Guanjie Zheng et al.Time series | KDD 2024 · ↗ | |
| 2024-03 | Graph Data Condensation via Self-expressive Graph Structure Reconstruction Zhanyu Liu, Chaolv Zeng, Guanjie ZhengGraphs | KDD 2024 · ↗ | |
| 2024-03 | MDC — Multisize Dataset Condensation Yang He, Lingao Xiao, Joey Tianyi Zhou et al. | ICLR 2024 · ↗ | |
| 2024-03 | LD3M — Unlocking Dataset Distillation with Diffusion Models Brian B. Moser, Federico Raue, Sebastian Palacio et al.notable | NeurIPS 2025 · ↗ | |
| 2024-02 | Model Pool — Improve Cross-Architecture Generalization on Dataset Distillation Binglin Zhou, Linhao Zhong, Wentao Chen | arXiv 2024 · ↗ | |
| 2024-02 | CTRL — Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching Tianle Zhang, Yuchen Zhang, Kun Wang et al.Graphs | arXiv 2024 · ↗ | |
| 2024-02 | Group Distributionally Robust Dataset Distillation with Risk Minimization Saeed Vahidian, Mingyu Wang, Jianyang Gu et al.notableTrustworthy DD | ICLR 2025 · ↗ | |
| 2024-02 | GEOM — Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching Yuchen Zhang, Tianle Zhang, Kai Wang et al.Graphsnotable | ICML 2024 · ↗ | |
| 2024-02 | A Survey on Graph Condensation Hongjia Xu, Liangliang Zhang, Yao Ma et al.GraphsSurvey | arXiv 2024 · ↗ | |
| 2024-01 | Dataset Condensation Driven Machine Unlearning Junaid Iqbal KhanApplication | arXiv 2024 · ↗ | |
| 2024-01 | Graph reduction survey — A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation Mohammad Hashemi, Shengbo Gong, Juntong Ni et al.GraphsSurvey | IJCAI 2024 · ↗ | |
| 2024-01 | IADD — Importance-Aware Adaptive Dataset Distillation Guang Li, Ren Togo, Takahiro Ogawa et al. | NN 2024 · ↗ | |
| 2024-01 | Graph condensation survey — Graph Condensation: A Survey Xinyi Gao, Junliang Yu, Tong Chen et al.GraphsSurvey | TKDE 2025 · ↗ | |
| 2024-01 | MedSynth — MedSynth: Leveraging Generative Model for Healthcare Data Sharing Renuga KanagaveluApplication | MICCAI 2024 · ↗ | |
| 2024-01 | Differentially Private Dataset Condensation Tianhang Zheng, Baochun LiTrustworthy DD | NDSS 2024 Workshop · ↗ | |
| 2024-01 | Textual Dataset Distillation via Language Model Embedding Yefan Tao, Luyang Kong, Andrey Kan et al.Text | EMNLP 2024 · ↗ | |
| 2024-01 | Information Compensation: A Fix for Any-scale Dataset Distillation Peng Sun | ICLR 2024 Workshop · ↗ | |
| 2024-01 | GSDD — GSDD: Generative Space Dataset Distillation for Image Super-resolution Haiyu Zhang, Shaolin Su, Yu Zhu et al.Dense predictionnotable | AAAI 2024 · ↗ | |
| 2024-01 | DCOD — Fetch and Forge: Efficient Dataset Condensation for Object Detection Ding Qi, Jian Li, Jinlong Peng et al.Dense prediction | NeurIPS 2024 · ↗ | |
| 2024-01 | D2M — Data-to-Model Distillation: Data-Efficient Learning Framework Ahmad Sajedi, Samir Khaki, Lucy Z. Liu et al. | ECCV 2024 · ↗ | |
| 2024-01 | AVDD — Audio-Visual Dataset Distillation Saksham Singh Kushwaha, Siva Sai Nagender Vasireddy, Kai Wang et al.Audio–visual & omnicore | TMLR 2024 · ↗ | |
| 2023-12 | MIM4DD — MIM4DD: Mutual Information Maximization for Dataset Distillation Yuzhang Shang, Zhihang Yuan, Yan Yan | NeurIPS 2023 · ↗ | |
| 2023-12 | M3D — M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy Hansong Zhang, Shikun Li, Pengju Wang et al.notable | AAAI 2024 · ↗ | |
| 2023-12 | DCFL — DCFL: Non-IID awareness Data Condensation aided Federated Learning Shaohan Sha, YaFeng SunApplication | IJCNN 2024 · ↗ | |
| 2023-12 | Dataset Distillation via Adversarial Prediction Matching Mingyang Chen, Bo Huang, Junda Lu et al. | arXiv 2023 · ↗ | |
| 2023-12 | ELF — Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study Lirui Zhao, Yuxin Zhang, Fei Chao et al.Analysis & theory | arXiv 2023 · ↗ | |
| 2023-12 | RDED — On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm Peng Sun, Bei Shi, Daiwei Yu et al.core | CVPR 2024 · ↗ | |
| 2023-12 | FedDG — Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents Yuqi Jia, Saeed Vahidian, Jingwei Sun et al.notableApplication | ECCV 2024 · ↗ | |
| 2023-12 | Static-dynamic video DD — Dancing with Still Images: Video Distillation via Static-Dynamic Disentanglement Ziyu Wang, Yue Xu, Cewu Lu et al.Videocore | CVPR 2024 · ↗ | |
| 2023-12 | A Theoretical Study of Dataset Distillation Zachary Izzo, James ZouAnalysis & theory | NeurIPS 2023 Workshop · ↗ | |
| 2023-11 | WMDD — Dataset Distillation via the Wasserstein Metric Haoyang Liu, Yijiang Li, Tiancheng Xing et al.notable | ICCV 2025 · ↗ | |
| 2023-11 | CDA — Dataset Distillation via Curriculum Data Synthesis in Large Data Era Zeyuan Yin, Zhiqiang Shennotable | TMLR 2024 · ↗ | |
| 2023-11 | G-VBSM — Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching Shitong Shao, Zeyuan Yin, Muxin Zhou et al.notable | CVPR 2024 · ↗ | |
| 2023-11 | Discovering Galaxy Features via Dataset Distillation Haowen Guan, Xuan Zhao, Zishi Wang et al.Application | NeurIPS 2023 Workshop · ↗ | |
| 2023-11 | Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective Ming-Yu Chung, Sheng-Yen Chou, Chia-Mu Yu et al.notableTrustworthy DD | ICLR 2024 · ↗ | |
| 2023-11 | Minimax Diffusion — Efficient Dataset Distillation via Minimax Diffusion Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev et al.core | CVPR 2024 · ↗ | |
| 2023-11 | Dataset Distillation in Latent Space Yuxuan Duan, Jianfu Zhang, Liqing Zhang | arXiv 2023 · ↗ | |
| 2023-11 | FreD — Frequency Domain-based Dataset Distillation Donghyeok Shin, Seungjae Shin, Il-Chul Moonnotable | NeurIPS 2023 · ↗ | |
| 2023-11 | RaT-BPTT — Embarassingly Simple Dataset Distillation Yunzhen Feng, Ramakrishna Vedantam, Julia Kempenotable | ICLR 2024 · ↗ | |
| 2023-11 | SeqMatch — Sequential Subset Matching for Dataset Distillation Jiawei Du, Qin Shi, Joey Tianyi Zhou | NeurIPS 2023 · ↗ | |
| 2023-10 | DREAM+ — DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching Yanqing Liu, Jianyang Gu, Kai Wang et al. | arXiv 2023 · ↗ | |
| 2023-10 | YOCO — You Only Condense Once: Two Rules for Pruning Condensed Datasets Yang He, Lingao Xiao, Joey Tianyi Zhou | NeurIPS 2023 · ↗ | |
| 2023-10 | Mirage — Mirage: Model-Agnostic Graph Distillation for Graph Classification Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana et al.Graphsnotable | ICLR 2024 · ↗ | |
| 2023-10 | GDEM — Graph Distillation with Eigenbasis Matching Yang Liu, Deyu Bo, Chuan ShiGraphsnotable | ICML 2024 · ↗ | |
| 2023-10 | SGDD — Does Graph Distillation See Like Vision Dataset Counterpart? Beining Yang, Kai Wang, Qingyun Sun et al.Graphsnotable | NeurIPS 2023 · ↗ | |
| 2023-10 | HMN — Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation Haizhong Zheng, Jiachen Sun, Shutong Wu et al. | ECCV 2024 · ↗ | |
| 2023-10 | Progressive DD — Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality Xuxi Chen, Yu Yang, Zhangyang Wang et al. | ICLR 2024 · ↗ | |
| 2023-10 | KRR-ST — Self-Supervised Dataset Distillation for Transfer Learning Dong Bok Lee, Seanie Lee, Joonho Ko et al.Pre-training & transfercore | ICLR 2024 · ↗ | |
| 2023-10 | DATM — Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching Ziyao Guo, Kai Wang, George Cazenavette et al.core | ICLR 2024 · ↗ | |
| 2023-10 | Can pre-trained models assist in dataset distillation? Yao Lu, Xuguang Chen, Yuchen Zhang et al.Analysis & theory | arXiv 2023 · ↗ | |
| 2023-09 | DataDAM — DataDAM: Efficient Dataset Distillation with Attention Matching Ahmad Sajedi, Samir Khaki, Ehsan Amjadian et al.notable | ICCV 2023 · ↗ | |
| 2023-09 | CaT — CaT: Balanced Continual Graph Learning with Graph Condensation Yilun Liu, Ruihong Qiu, Zi HuangGraphsApplication | ICDM 2023 · ↗ | |
| 2023-09 | Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine SouloumiacApplication | ICASSP 2024 · ↗ | |
| 2023-09 | Dataset Condensation via Generative Model David Junhao Zhang, Heng Wang, Chuhui Xue et al. | arXiv 2023 · ↗ | |
| 2023-08 | DQ — Dataset Quantization Daquan Zhou, Kai Wang, Jianyang Gu et al.core | ICCV 2023 · ↗ | |
| 2023-08 | MTT-VL — Vision-Language Dataset Distillation Xindi Wu, Byron Zhang, Zhiwei Deng et al.Vision–languagelandmark | TMLR 2024 · ↗ | |
| 2023-07 | Rethinking Data Distillation: Do Not Overlook Calibration Dongyao Zhu, Bowen Lei, Jie Zhang et al.notableTrustworthy DD | ICCV 2023 · ↗ | |
| 2023-07 | IDM — Improved Distribution Matching for Dataset Condensation Ganlong Zhao, Guanbin Li, Yipeng Qin et al.core | CVPR 2023 · ↗ | |
| 2023-07 | TrustDD — Towards Trustworthy Dataset Distillation Shijie Ma, Fei Zhu, Zhen Cheng et al.Trustworthy DD | PR 2024 · ↗ | |
| 2023-06 | SRe2L — Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective Zeyuan Yin, Eric Xing, Zhiqiang Shenlandmark | NeurIPS 2023 · ↗ | |
| 2023-06 | SFGC — Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data Xin Zheng, Miao Zhang, Chunyang Chen et al.Graphsnotable | NeurIPS 2023 · ↗ | |
| 2023-05 | IEM — Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching Tao Feng, Jie Zhang, Huashan Liu et al.Application | arXiv 2023 · ↗ | |
| 2023-05 | Gold from Ores — Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation Yue Xu, Yong-Lu Li, Kaitong Cui et al. | ECCV 2024 · ↗ | |
| 2023-05 | SSD — Summarizing Stream Data for Memory-Constrained Online Continual Learning Jianyang Gu, Kai Wang, Wei Jiang et al.notableApplication | AAAI 2024 · ↗ | |
| 2023-05 | Size & approximation error — On the Size and Approximation Error of Distilled Sets Alaa Maalouf, Murad Tukan, Noel Loo et al.Analysis & theory | NeurIPS 2023 · ↗ | |
| 2023-05 | A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness Zongxiong Chen, Jiahui Geng, Derui Zhu et al.Evaluation & benchmark | arXiv 2023 · ↗ | |
| 2023-05 | Geng et al. survey — A Survey on Dataset Distillation: Approaches, Applications and Future Directions Jiahui Geng, Zongxiong Chen, Yuandou Wang et al.coreSurvey | IJCAI 2023 · ↗ | |
| 2023-05 | GLaD — Generalizing Dataset Distillation via Deep Generative Prior George Cazenavette, Tongzhou Wang, Antonio Torralba et al.landmark | CVPR 2023 · ↗ | |
| 2023-03 | LCMat — Loss-Curvature Matching for Dataset Selection and Condensation Seungjae Shin, Heesun Bae, Donghyeok Shin et al. | AISTATS 2023 · ↗ | |
| 2023-03 | DiM — DiM: Distilling Dataset into Generative Model Kai Wang, Jianyang Gu, Daquan Zhou et al.core | arXiv 2023 · ↗ | |
| 2023-03 | FedLGD — Federated Learning on Virtual Heterogeneous Data with Local-global Distillation Chun-Yin Huang, Ruinan Jin, Can Zhao et al.Application | TMLR 2024 · ↗ | |
| 2023-02 | DREAM — DREAM: Efficient Dataset Distillation by Representative Matching Yanqing Liu, Jianyang Gu, Kai Wang et al. | ICCV 2023 · ↗ | |
| 2023-02 | RCIG — Dataset Distillation with Convexified Implicit Gradients Noel Loo, Ramin Hasani, Mathias Lechner et al.notable | ICML 2023 · ↗ | |
| 2023-02 | Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation Noel Loo, Ramin Hasani, Mathias Lechner et al.notableTrustworthy DD | ICLR 2024 · ↗ | |
| 2023-02 | FedLAP-DP — FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations Hui-Po Wang, Dingfan Chen, Raouf Kerkouche et al.Application | arXiv 2023 · ↗ | |
| 2023-01 | DP-KIP-ScatterNet — Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation Margarita Vinaroz, Mi Jung ParkTrustworthy DD | arXiv 2023 · ↗ | |
| 2023-01 | Yu et al. review — Dataset Distillation: A Comprehensive Review Ruonan Yu, Songhua Liu, Xinchao WangcoreSurvey | TPAMI 2023 · ↗ | |
| 2023-01 | Lei & Tao survey — A Comprehensive Survey of Dataset Distillation Shiye Lei, Dacheng TaocoreSurvey | TPAMI 2023 · ↗ | |
| 2023-01 | Sachdeva & McAuley survey — Data Distillation: A Survey Noveen Sachdeva, Julian McAuleyGraphsOther datacoreSurvey | TMLR 2023 · ↗ | |
| 2023-01 | BIB — Bidirectional Learning for Offline Model-based Biological Sequence Design Can Chen, Yingxue Zhang, Xue Liu et al.Other dataApplication | ICML 2023 · ↗ | |
| 2023-01 | Backdoor attacks on DD — Backdoor Attacks Against Dataset Distillation Yugeng Liu, Zheng Li, Michael Backes et al.notableTrustworthy DD | NDSS 2023 · ↗ | |
| 2023-01 | Fair Graph Distillation Qizhang Feng, Zhimeng Jiang, Ruiquan Li et al.GraphsTrustworthy DD | NeurIPS 2023 · ↗ | |
| 2023-01 | Dataset Distillation for Medical Dataset Sharing Guang Li, Ren Togo, Takahiro Ogawa et al.Application | AAAI 2023 Workshop · ↗ | |
| 2023-01 | GDD-FL — Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation Yuchen Tian, Jiacheng Wang, Yueming Jin et al.Application | MICCAI 2023 Workshop · ↗ | |
| 2023-01 | An Efficient Dataset Condensation Plugin and Its Application to Continual Learning Enneng Yang, Li Shen, Zhenyi Wang et al.notable | NeurIPS 2023 · ↗ | |
| 2023-01 | Translative pre-training — Few-Shot Dataset Distillation via Translative Pre-Training Songhua Liu, Xinchao Wang | ICCV 2023 · ↗ | |
| 2023-01 | SPEED — Sparse Parameterization for Epitomic Dataset Distillation Xing Wei, Anjia Cao, Funing Yang et al. | NeurIPS 2023 · ↗ | |
| 2023-01 | Slimmable DC — Slimmable Dataset Condensation Songhua Liu, Jingwen Ye, Runpeng Yu et al. | CVPR 2023 · ↗ | |
| 2023-01 | MGDD — MGDD: A Meta Generator for Fast Dataset Distillation Songhua Liu, Xinchao Wang | NeurIPS 2023 · ↗ | |
| 2023-01 | KIDD — Kernel Ridge Regression-Based Graph Dataset Distillation Zhe Xu, Yuzhong Chen, Menghai PanGraphs | KDD 2023 · ↗ | |
| 2023-01 | CGM — Gradient Matching for Categorical Data Distillation in CTR Prediction Cheng Wang, Jiacheng Sun, Zhenhua Dong et al.Other data | RecSys 2023 · ↗ | |
| 2023-01 | Generative Dataset Distillation Jovan CicvarićOther data | University of Tübingen 2023 · ↗ | |
| 2023-01 | Dataset Distillation with Attention Labels for Fine-tuning BERT Aru Maekawa, Naoki Kobayashi, Kotaro Funakoshi et al.Text | ACL 2023 · ↗ | |
| 2023-01 | Data-efficient Neural Network Training with Dataset Condensation Bo Zhao | The University of Edinburgh 2023 · ↗ | |
| 2022-12 | Acc-DD — Accelerating Dataset Distillation via Model Augmentation Lei Zhang, Jie Zhang, Bowen Lei et al. | CVPR 2023 · ↗ | |
| 2022-11 | FTD — Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation Jiawei Du, Yidi Jiang, Vincent Y. F. Tan et al.notable | CVPR 2023 · ↗ | |
| 2022-11 | DynaFed — DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics Renjie Pi, Weizhong Zhang, Yueqi Xie et al.notableApplication | CVPR 2023 · ↗ | |
| 2022-11 | Towards Robust Dataset Learning Yihan Wu, Xinda Li, Florian Kerschbaum et al.Trustworthy DD | arXiv 2022 · ↗ | |
| 2022-11 | TESLA — Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory Justin Cui, Ruochen Wang, Si Si et al.core | ICML 2023 · ↗ | |
| 2022-11 | Private Set Generation with Discriminative Information Dingfan Chen, Raouf Kerkouche, Mario FritznotableTrustworthy DD | NeurIPS 2022 · ↗ | |
| 2022-10 | HaBa — Dataset Distillation via Factorization Songhua Liu, Kai Wang, Xingyi Yang et al.core | NeurIPS 2022 · ↗ | |
| 2022-10 | RFAD — Efficient Dataset Distillation Using Random Feature Approximation Noel Loo, Ramin Hasani, Alexander Amini et al.notable | NeurIPS 2022 · ↗ | |
| 2022-10 | On Divergence Measures for Bayesian Pseudocoresets Balhae Kim, Jungwon Choi, Seanie Lee et al.Analysis & theory | NeurIPS 2022 · ↗ | |
| 2022-09 | Parameter pruning DD — Dataset Distillation Using Parameter Pruning Guang Li, Ren Togo, Takahiro Ogawa et al. | IEICE Transactions on Fundamentals 2023 · ↗ | |
| 2022-09 | No Free Lunch — No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy" Nicholas Carlini, Vitaly Feldman, Milad NasrnotableAnalysis & theory | arXiv 2022 · ↗ | |
| 2022-09 | Meta Knowledge Condensation for Federated Learning Ping Liu, Xin Yu, Joey Tianyi ZhounotableApplication | ICLR 2023 · ↗ | |
| 2022-09 | Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing Guang Li, Ren Togo, Takahiro Ogawa et al.Application | CMPB 2022 · ↗ | |
| 2022-09 | BDI — Bidirectional Learning for Offline Infinite-width Model-based Optimization Can Chen, Yingxue Zhang, Jie Fu et al.Other dataApplication | NeurIPS 2022 · ↗ | |
| 2022-08 | FedD3 — Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments Rui Song, Dai Liu, Dave Zhenyu Chen et al.Application | IJCNN 2023 · ↗ | |
| 2022-08 | KFS — Dataset Condensation with Latent Space Knowledge Factorization and Sharing Hae Beom Lee, Dong Bok Lee, Sung Ju Hwang | arXiv 2022 · ↗ | |
| 2022-07 | Can we achieve robustness from data alone? Nikolaos Tsilivis, Jingtong Su, Julia KempeTrustworthy DD | ICML 2022 Workshop · ↗ | |
| 2022-07 | FedDM — FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning Yuanhao Xiong, Ruochen Wang, Minhao Cheng et al.notableApplication | CVPR 2023 · ↗ | |
| 2022-07 | DC-BENCH — DC-BENCH: Dataset Condensation Benchmark Justin Cui, Ruochen Wang, Si Si et al.coreEvaluation & benchmark | NeurIPS 2022 · ↗ | |
| 2022-07 | On Implicit Bias in Overparameterized Bilevel Optimization Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa et al.Analysis & theory | ICML 2022 · ↗ | |
| 2022-06 | GCDM — Graph Condensation via Receptive Field Distribution Matching Mengyang Liu, Shanchuan Li, Xinshi Chen et al.Graphs | arXiv 2022 · ↗ | |
| 2022-06 | OLCGM — Sample Condensation in Online Continual Learning Mattia Sangermano, Antonio Carta, Andrea Cossu et al.Application | IJCNN 2022 · ↗ | |
| 2022-06 | PRANC — PRANC: Pseudo RAndom Networks for Compacting deep models Parsa Nooralinejad, Ali Abbasi, Soroush Abbasi Koohpayegani et al. | ICCV 2023 · ↗ | |
| 2022-06 | DosCond — Condensing Graphs via One-Step Gradient Matching Wei Jin, Xianfeng Tang, Haoming Jiang et al.Graphs | KDD 2022 · ↗ | |
| 2022-06 | Addressable memories — Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks Zhiwei Deng, Olga Russakovskynotable | NeurIPS 2022 · ↗ | |
| 2022-06 | Distill-CF — Infinite Recommendation Networks: A Data-Centric Approach Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu et al.Other datanotable | NeurIPS 2022 · ↗ | |
| 2022-06 | Privacy for Free — Privacy for Free: How does Dataset Condensation Help Privacy? Tian Dong, Bo Zhao, Lingjuan LyunotableTrustworthy DD | ICML 2022 · ↗ | |
| 2022-06 | FRePo — Dataset Distillation using Neural Feature Regression Yongchao Zhou, Ehsan Nezhadarya, Jimmy Bacore | NeurIPS 2022 · ↗ | |
| 2022-05 | IDC — Dataset Condensation via Efficient Synthetic-Data Parameterization Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh et al.core | ICML 2022 · ↗ | |
| 2022-04 | DeepCore — DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning Chengcheng Guo, Bo Zhao, Yanbing BaiSelection baseline | DEXA 2022 · ↗ | |
| 2022-04 | IT-GAN — Synthesizing Informative Training Samples with GAN Bo Zhao, Hakan Bilencore | NeurIPS 2022 Workshop · ↗ | |
| 2022-04 | FedSynth — FedSynth: Gradient Compression via Synthetic Data in Federated Learning Shengyuan Hu, Jack Goetz, Kshitiz Malik et al.Application | arXiv 2022 · ↗ | |
| 2022-03 | MTT — Dataset Distillation by Matching Training Trajectories George Cazenavette, Tongzhou Wang, Antonio Torralba et al.landmark | CVPR 2022 · ↗ | |
| 2022-03 | Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation Dmitry Medvedev, Alexander D'yakonov | AIST 2021 · ↗ | |
| 2022-03 | CAFE — CAFE: Learning to Condense Dataset by Aligning Features Kai Wang, Bo Zhao, Xiangyu Peng et al.core | CVPR 2022 · ↗ | |
| 2022-02 | DCC — Dataset Condensation with Contrastive Signals Saehyung Lee, Sanghyuk Chun, Sangwon Jung et al. | ICML 2022 · ↗ | |
| 2022-01 | Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation George Cazenavette, Tongzhou Wang, Antonio Torralba et al.Application | CVPR 2022 Workshop · ↗ | |
| 2022-01 | ADD-GNN — Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation Yulan ChenApplication | ICIP 2022 · ↗ | |
| 2021-12 | DENSE — DENSE: Data-Free One-Shot Federated Learning Jie Zhang, Chen Chen, Bo Li et al.notableApplication | NeurIPS 2022 · ↗ | |
| 2021-10 | GCond — Graph Condensation for Graph Neural Networks Wei Jin, Lingxiao Zhao, Shichang Zhang et al.Graphslandmark | ICLR 2022 · ↗ | |
| 2021-10 | DM — Dataset Condensation with Distribution Matching Bo Zhao, Hakan Bilenlandmark | WACV 2023 · ↗ | |
| 2021-07 | KIP-ConvNet — Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen, Roman Novak, Lechao Xiao et al.notable | NeurIPS 2021 · ↗ | |
| 2021-07 | EL2N / GraNd — Deep Learning on a Data Diet: Finding Important Examples Early in Training Mansheej Paul, Surya Ganguli, Gintare Karolina DziugaiteSelection baseline | NeurIPS 2021 · ↗ | |
| 2021-04 | Data Distillation for Text Classification Yongqi Li, Wenjie LiText | arXiv 2021 · ↗ | |
| 2021-04 | Gastric SLDD — Soft-Label Anonymous Gastric X-ray Image Distillation Guang Li, Ren Togo, Takahiro Ogawa et al.Application | ICIP 2020 · ↗ | |
| 2021-03 | Distilled Replay: Overcoming Forgetting through Synthetic Samples Andrea Rosasco, Antonio Carta, Andrea Cossu et al.Application | IJCAI 2021 Workshop · ↗ | |
| 2021-02 | CCMCL — Condensed Composite Memory Continual Learning Felix Wiewel, Bin YangApplication | IJCNN 2021 · ↗ | |
| 2021-02 | DSA — Dataset Condensation with Differentiable Siamese Augmentation Bo Zhao, Hakan Bilenlandmark | ICML 2021 · ↗ | |
| 2020-10 | KIP — Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen, Zhourong Chen, Jaehoon Leelandmark | ICLR 2021 · ↗ | |
| 2020-10 | New Properties of the Data Distillation Method When Working With Tabular Data Dmitry Medvedev, Alexander D'yakonovOther dataAnalysis & theory | AIST 2020 · ↗ | |
| 2020-09 | DOSFL — Distilled One-Shot Federated Learning Yanlin Zhou, George Pu, Xiyao Ma et al.Application | arXiv 2020 · ↗ | |
| 2020-08 | Federated Learning via Synthetic Data Jack Goetz, Ambuj TewariApplication | arXiv 2020 · ↗ | |
| 2020-06 | Learned labels — Flexible Dataset Distillation: Learn Labels Instead of Images Ondrej Bohdal, Yongxin Yang, Timothy HospedalesText | NeurIPS 2020 Workshop · ↗ | |
| 2020-06 | DC — Dataset Condensation with Gradient Matching Bo Zhao, Konda Reddy Mopuri, Hakan Bilenlandmark | ICLR 2021 · ↗ | |
| 2020-04 | Reducing catastrophic forgetting with learning on synthetic data Wojciech Masarczyk, Ivona TautkuteApplication | CVPR 2020 Workshop · ↗ | |
| 2019-12 | GTN — Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data Felipe Petroski Such, Aditya Rawal, Joel Lehman et al.notableApplication | ICML 2020 · ↗ | |
| 2019-11 | Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine, Paul Vicol, David DuvenaudAnalysis & theory | AISTATS 2020 · ↗ | |
| 2019-10 | Soft-label DD — Soft-Label Dataset Distillation and Text Dataset Distillation Ilia Sucholutsky, Matthias SchonlauText | IJCNN 2021 · ↗ | |
| 2018-12 | Forgetting — An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes et al.notableSelection baseline | ICLR 2019 · ↗ | |
| 2018-11 | DD — Dataset Distillation Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba et al.landmark | arXiv 2018 · ↗ | |
| 2017-08 | k-Center coreset — Active Learning for Convolutional Neural Networks: A Core-Set Approach Ozan Sener, Silvio SavaresenotableSelection baseline | ICLR 2018 · ↗ | |
| 2015-02 | Hypergradient — Gradient-based Hyperparameter Optimization through Reversible Learning Dougal Maclaurin, David Duvenaud, Ryan P. AdamsAnalysis & theory | ICML 2015 · ↗ | |
| 2009-01 | Herding — Herding Dynamical Weights to Learn Max WellingnotableSelection baseline | ICML 2009 · ↗ |