13.6.5 Dataset Distillation, Dataset Summary, Dataset Quantization

Yu, R.N.[Ruo-Nan], Liu, S.H.[Song-Hua], Wang, X.C.[Xin-Chao],
Dataset Distillation: A Comprehensive Review,
PAMI(46), No. 1, January 2024, pp. 150-170.
IEEE DOI 2312
Dataset condensation. Reduse to what matters. BibRef

Lei, S.[Shiye], Tao, D.C.[Da-Cheng],
A Comprehensive Survey of Dataset Distillation,
PAMI(46), No. 1, January 2024, pp. 17-32.
IEEE DOI 2312
BibRef

Jin, H.D.[Hyun-Dong], Kim, E.[Eunwoo],
Dataset condensation with coarse-to-fine regularization,
PRL(188), 2025, pp. 178-184.
Elsevier DOI 2502
Dataset condensation, Representation learning BibRef

Wu, Y.F.[Yi-Fan], Du, J.W.[Jia-Wei], Liu, P.[Ping], Lin, Y.W.[Yue-Wei], Xu, W.[Wei], Cheng, W.Q.[Wen-Qing],
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation,
IP(34), 2025, pp. 2052-2066.
IEEE DOI 2504
Robustness, Benchmark testing, Training, Accuracy, Data augmentation, Pipelines, Computational modeling, Loading, Iterative methods, benchmark BibRef

Ma, Z.H.[Zhi-Heng], Cao, A.[Anjia], Yang, F.[Funing], Gong, Y.H.[Yi-Hong], Wei, X.[Xing],
Curriculum Dataset Distillation,
IP(34), 2025, pp. 4176-4187.
IEEE DOI Code:
WWW Link. 2507
Synthetic data, Optimization, Training, Neural networks, Mathematical models, Scalability, Artificial intelligence, curriculum learning BibRef

Huang, T.F.[Ting-Feng], Lin, Y.H.[Yu-Hsun],
Drop2Sparse: Improving Dataset Distillation via Sparse Model,
CirSysVideo(35), No. 8, August 2025, pp. 7568-7578.
IEEE DOI 2508
Training, Synthetic data, Accuracy, Image coding, Computational modeling, Integrated circuit modeling, Runtime, model sparsification BibRef

Cui, X.[Xiao], Qin, Y.[Yulei], Zhou, W.G.[Wen-Gang], Li, H.S.[Hong-Sheng], Li, H.Q.[Hou-Qiang],
OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation,
CVPR25(15245-15254)
IEEE DOI 2508
Deep learning, Computational modeling, Optical variables measurement, Minimization, Geometrical optics, Synthetic data BibRef

Cho, S.[Sunwoo], Jung, Y.[Yejin], Cho, N.I.[Nam Ik], Soh, J.W.[Jae Woong],
Dataset Distillation for Super-Resolution Without Class Labels and Pre-Trained Models,
SPLetters(32), 2025, pp. 3700-3704.
IEEE DOI 2510
Training, Diffusion models, Computational modeling, Superresolution, Data models, Generative adversarial networks, dataset distillation BibRef

Zhang, J.X.[Jing-Xuan], Chen, Z.H.[Zhi-Hua], Dai, L.[Lei],
Unleashing the Power of Each Distilled Image,
IP(34), 2025, pp. 7050-7064.
IEEE DOI 2511
Training, Synthetic data, Artificial neural networks, Overfitting, Data models, Computational modeling, image classification BibRef

Zhang, H.L.[Hong-Liang], An, X.Q.[Xiao-Qi], Lian, J.W.[Jia-Wei], Luo, L.[Lei], Yang, J.[Jian],
CoMPR: Efficient point cloud dataset condensation via bidirectional matching and point recycling,
PR(172), 2026, pp. 112494.
Elsevier DOI 2512
Dataset condensation, Point cloud dataset condensation, Point recycling, Bidirectional matching, BibRef

Li, Z.[Zhe], Cechnicka, S.[Sarah], Ouyang, C.[Cheng], Breininger, K.[Katharina], Schüffler, P.[Peter], Kainz, B.[Bernhard],
Stochastic latent feature distillation: Enhancing dataset distillation via structured uncertainty modeling,
JVCIR(113), 2025, pp. 104623.
Elsevier DOI 2512
Dataset distillation, Stochastic method, Image classification BibRef

Qi, D.[Ding], Li, J.[Jian], Gao, J.[Junyao], Dou, S.G.[Shu-Guang], Tai, Y.[Ying], Hu, J.L.[Jian-Long], Zhao, B.[Bo], Wang, Y.B.[Ya-Biao], Wang, C.J.[Cheng-Jie], Zhao, C.R.[Cai-Rong],
Towards Universal Dataset Distillation via Task-Driven Diffusion,
CVPR25(10557-10566)
IEEE DOI 2508
Training, Image segmentation, Costs, Image synthesis, Diffusion processes, Diffusion models, Optimization, Image classification BibRef

Tran, M.T.[Minh-Tuan], Le, T.[Trung], Le, X.M.[Xuan-May], Do, T.T.[Thanh-Toan], Phung, D.[Dinh],
Enhancing Dataset Distillation via Non-Critical Region Refinement,
CVPR25(10015-10024)
IEEE DOI Code:
WWW Link. 2508
Training, Codes, Memory management, Complexity theory, Knowledge transfer, Synthetic data, dataset distillation, efficient machine learning BibRef

Shi, Y.[Yudi], Di, S.Z.[Shang-Zhe], Chen, Q.[Qirui], Xie, W.[Weidi],
Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation,
CVPR25(8523-8533)
IEEE DOI 2508
Grounding, Computational modeling, Large language models, Benchmark testing, Cognition, Videos BibRef

Chen, Y.[Yanda], Chen, G.[Gongwei], Zhang, M.[Miao], Guan, W.[Weili], Nie, L.Q.[Li-Qiang],
Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation,
CVPR25(20437-20446)
IEEE DOI Code:
WWW Link. 2508
Training, Degradation, Codes, Accuracy, Scalability, Synthetic data, dataset distillation, curriculum learning, high-ipc BibRef

Frank, L.[Logan], Davis, J.[Jim],
What Makes a Good Dataset for Knowledge Distillationƒ,
CVPR25(23755-23764)
IEEE DOI Code:
WWW Link. 2508
Training, Source coding, Perturbation methods, Computational modeling, Data models, out-of-distribution knowledge distillation BibRef

Zhong, W.L.[Wen-Liang], Tang, H.Y.[Hao-Yu], Zheng, Q.H.[Qing-Hai], Xu, M.Z.[Ming-Zhu], Hu, Y.P.[Yu-Peng], Guan, W.[Weili],
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory,
CVPR25(25581-25589)
IEEE DOI 2508
Training, Fitting, Memory management, Training data, Stochastic processes, Trajectory, Optimization, Synthetic data, deep learning BibRef

Wang, S.B.[Shao-Bo], Yang, Y.C.[Yi-Cun], Liu, Z.Y.[Zhi-Yuan], Sun, C.H.[Cheng-Hao], Hu, X.M.[Xu-Ming], He, C.H.[Cong-Hui], Zhang, L.F.[Lin-Feng],
Dataset Distillation with Neural Characteristic Function: A Minmax Perspective,
CVPR25(25570-25580)
IEEE DOI Code:
WWW Link. 2508
Measurement, Image coding, Scalability, Neural networks, Graphics processing units, Performance gain, characteristic function BibRef

Zhao, Z.H.[Zheng-Hao], Wang, H.X.[Hao-Xuan], Shang, Y.Z.[Yu-Zhang], Wang, K.[Kai], Yan, Y.[Yan],
Distilling Long-tailed Datasets,
CVPR25(30609-30618)
IEEE DOI Code:
WWW Link. 2508
Training, Heavily-tailed distribution, Codes, Trajectory, Reliability, Synthetic data, dataset distillation BibRef

Zhong, X.H.[Xin-Hao], Fang, H.[Hao], Chen, B.[Bin], Gu, X.[Xulin], Qiu, M.[Meikang], Qi, S.H.[Shu-Han], Xia, S.T.[Shu-Tao],
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation,
CVPR25(30462-30471)
IEEE DOI Code:
WWW Link. 2508
Measurement, Codes, Accuracy, Transforms, Generative adversarial networks, Diffusion models, Optimization, hierarchical BibRef

Wang, K.[Kai], Li, Z.[Zekai], Cheng, Z.Q.[Zhi-Qi], Khaki, S.[Samir], Sajedi, A.[Ahmad], Vedantam, R.[Ramakrishna], Plataniotis, K.N.[Konstantinos N], Hauptmann, A.[Alexander], You, Y.[Yang],
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios,
CVPR25(30451-30461)
IEEE DOI 2508
Codes, Filtering, Benchmark testing, efficient deep learning, dataset distillatiom BibRef

Malakshan, S.R.[Sahar Rahimi], Saadabadi, M.S.E.[Mohammad Saeed Ebrahimi], Dabouei, A.[Ali], Nasrabadi, N.M.[Nasser M.],
Decomposed Distribution Matching in Dataset Condensation,
WACV25(7112-7122)
IEEE DOI 2505
Training, Degradation, Image resolution, Codes, Accuracy, Artificial neural networks, intra-class diversity BibRef

Kang, S.[Seoungyoon], Lim, Y.[Youngsun], Shim, H.J.[Hyun-Jung],
Label-Augmented Dataset Distillation,
WACV25(1457-1466)
IEEE DOI 2505
Training, Accuracy, Semantics, Image representation, Robustness, Image storage, Synthetic data, dataset distillation, synthetic dataset BibRef

Moon, J.Y.[Jun-Yeong], Kim, J.U.[Jung Uk], Park, G.M.[Gyeong-Moon],
Towards Model-agnostic Dataset Condensation by Heterogeneous Models,
ECCV24(XXIX: 234-250).
Springer DOI 2412
BibRef

Zhao, Z.H.[Zheng-Hao], Shang, Y.Z.[Yu-Zhang], Wu, J.[Junyi], Yan, Y.[Yan],
Dataset Quantization with Active Learning Based Adaptive Sampling,
ECCV24(LX: 346-362).
Springer DOI 2412
BibRef

Zheng, H.Z.[Hai-Zhong], Sun, J.C.[Jia-Chen], Wu, S.[Shutong], Kailkhura, B.[Bhavya], Mao, Z.M.[Z. Morley], Xiao, C.W.[Chao-Wei], Prakash, A.[Atul],
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation,
ECCV24(XXIV: 166-182).
Springer DOI 2412
BibRef

Xu, Y.[Yue], Li, Y.L.[Yong-Lu], Cui, K.[Kaitong], Wang, Z.Y.[Zi-Yu], Lu, C.[Cewu], Tai, Y.W.[Yu-Wing], Tang, C.K.[Chi-Keung],
Distill Gold from Massive Ores: Bi-level Data Pruning Towards Efficient Dataset Distillation,
ECCV24(XX: 240-257).
Springer DOI 2412
BibRef

Yu, R.N.[Ruo-Nan], Liu, S.[Songhua], Ye, J.W.[Jing-Wen], Wang, X.C.[Xin-Chao],
Teddy: Efficient Large-scale Dataset Distillation via Taylor-approximated Matching,
ECCV24(XLVI: 1-17).
Springer DOI 2412
BibRef

Yang, S.L.[Shao-Lei], Cheng, S.[Shen], Hong, M.B.[Ming-Bo], Fan, H.Q.[Hao-Qiang], Wei, X.[Xing], Liu, S.C.[Shuai-Cheng],
Neural Spectral Decomposition for Dataset Distillation,
ECCV24(LII: 275-290).
Springer DOI 2412
BibRef

Son, B.[Byunggwan], Oh, Y.[Youngmin], Baek, D.[Donghyeon], Ham, B.[Bumsub],
FYI: Flip Your Images for Dataset Distillation,
ECCV24(L: 214-230).
Springer DOI 2412
BibRef

Liu, D.[Dai], Gu, J.D.[Jin-Dong], Cao, H.[Hu], Trinitis, C.[Carsten], Schulz, M.[Martin],
Dataset Distillation by Automatic Training Trajectories,
ECCV24(LXXXVII: 334-351).
Springer DOI 2412
BibRef

Jia, Y.Q.[Yu-Qi], Vahidian, S.[Saeed], Sun, J.W.[Jing-Wei], Zhang, J.Y.[Jian-Yi], Kungurtsev, V.[Vyacheslav], Gong, N.Z.Q.[Neil Zhen-Qiang], Chen, Y.R.[Yi-Ran],
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents,
ECCV24(LXXVIII: 18-33).
Springer DOI 2412
BibRef

Ye, J.W.[Jing-Wen], Yu, R.N.[Ruo-Nan], Liu, S.[Songhua], Wang, X.C.[Xin-Chao],
Distilled Datamodel with Reverse Gradient Matching,
CVPR24(11954-11963)
IEEE DOI 2410
Training, Computational modeling, Data integrity, Training data, Reinforcement learning, Data models BibRef

Deng, W.X.[Wen-Xiao], Li, W.B.[Wen-Bin], Ding, T.Y.[Tian-Yu], Wang, L.[Lei], Zhang, H.G.[Hong-Guang], Huang, K.[Kuihua], Huo, J.[Jing], Gao, Y.[Yang],
Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation,
CVPR24(17057-17066)
IEEE DOI Code:
WWW Link. 2410
Training, Deep learning, Face recognition, Focusing, Computational efficiency, Inter-feature BibRef

Zhu, D.Y.[Dong-Yao], Fang, Y.B.[Yan-Bo], Lei, B.[Bowen], Xie, Y.Q.[Yi-Qun], Xu, D.K.[Dong-Kuan], Zhang, J.[Jie], Zhang, R.[Ruqi],
Rethinking Data Distillation: Do Not Overlook Calibration,
ICCV23(4912-4922)
IEEE DOI 2401
BibRef

van Noord, N.[Nanne],
Prototype-based Dataset Comparison,
ICCV23(1944-1954)
IEEE DOI Code:
WWW Link. 2401
BibRef

Sajedi, A.[Ahmad], Khaki, S.[Samir], Amjadian, E.[Ehsan], Liu, L.Z.[Lucy Z.], Lawryshyn, Y.A.[Yuri A.], Plataniotis, K.N.[Konstantinos N.],
DataDAM: Efficient Dataset Distillation with Attention Matching,
ICCV23(17051-17061)
IEEE DOI 2401
BibRef

Zhou, D.[Daquan], Wang, K.[Kai], Gu, J.Y.[Jian-Yang], Peng, X.Y.[Xiang-Yu], Lian, D.Z.[Dong-Ze], Zhang, Y.F.[Yi-Fan], You, Y.[Yang], Feng, J.S.[Jia-Shi],
Dataset Quantization,
ICCV23(17159-17170)
IEEE DOI 2401
BibRef

Liu, Y.Q.[Yan-Qing], Gu, J.Y.[Jian-Yang], Wang, K.[Kai], Zhu, Z.[Zheng], Jiang, W.[Wei], You, Y.[Yang],
DREAM: Efficient Dataset Distillation by Representative Matching,
ICCV23(17268-17278)
IEEE DOI 2401
BibRef

Liu, S.[Songhua], Wang, X.C.[Xin-Chao],
Few-Shot Dataset Distillation via Translative Pre-Training,
ICCV23(18608-18618)
IEEE DOI 2401
BibRef

Mazumder, A.[Alokendu], Baruah, T.[Tirthajit], Singh, A.K.[Akash Kumar], Murthy, P.K.[Pagadala Krishna], Pattanaik, V.[Vishwajeet], Rathore, P.[Punit],
DeepVAT: A Self-Supervised Technique for Cluster Assessment in Image Datasets,
VIPriors23(187-195)
IEEE DOI 2401
BibRef

Zhang, L.[Lei], Zhang, J.[Jie], Lei, B.[Bowen], Mukherjee, S.[Subhabrata], Pan, X.[Xiang], Zhao, B.[Bo], Ding, C.[Caiwen], Li, Y.[Yao], Xu, D.[Dongkuan],
Accelerating Dataset Distillation via Model Augmentation,
CVPR23(11950-11959)
IEEE DOI 2309
smaller but efficient synthetic training datasets from large ones BibRef

Cazenavette, G.[George], Wang, T.Z.[Tong-Zhou], Torralba, A.[Antonio], Efros, A.A.[Alexei A.], Zhu, J.Y.[Jun-Yan],
Generalizing Dataset Distillation via Deep Generative Prior,
CVPR23(3739-3748)
IEEE DOI 2309
BibRef

Wang, Z.J.[Zi-Jia], Yang, W.B.[Wen-Bin], Liu, Z.S.[Zhi-Song], Chen, Q.[Qiang], Ni, J.C.[Jia-Cheng], Jia, Z.[Zhen],
Gift from Nature: Potential Energy Minimization for Explainable Dataset Distillation,
MLCSA22(240-255).
Springer DOI 2307
BibRef

Cazenavette, G.[George], Wang, T.Z.[Tong-Zhou], Torralba, A.[Antonio], Efros, A.A.[Alexei A.], Zhu, J.Y.[Jun-Yan],
Dataset Distillation by Matching Training Trajectories,
CVPR22(10708-10717)
IEEE DOI 2210
BibRef
Earlier: VDU22(4749-4758)
IEEE DOI 2210
Training, Visualization, Trajectory, Task analysis, Unsupervised learning, Pattern matching, Self- semi- meta- unsupervised learning Training, Visualization, Trajectory, Task analysis, Pattern matching BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Fine Tuning, Fine-Tuning, Pre-Training, Zero-Shot, One-Shot .