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

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 .