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Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs.
Mathematical methods of organizing and planning production
Leonid V Kantorovich · 1960
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Convex optimization
Stephen P Boyd and Lieven Vandenberghe · 2004
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Optimal Transport: Old and New
Cédric Villani · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Barycenters in the wasserstein space
Martial Agueh and Guillaume Carlier · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Fast computation of wasserstein barycenters
Marco Cuturi and Arnaud Doucet · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
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Minimax estimation of maximum mean discrepancy with radial kernels
Ilya O Tolstikhin, Bharath K. Sriperumbudur, and Bernhard Schölkopf · 2016
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Imagenette dataset, 2019
Jeremy Howard · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Federated learning via synthetic data
Jack Goetz and Ambuj Tewari · 2020
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Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune · 2020
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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Compound batch normalization for long-tailed image classification
Lechao Cheng, Chaowei Fang, Dingwen Zhang, Guanbin Li, and Gang Huang · 2022
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Remember the past: Distilling datasets into addressable memories for neural networks
Zhiwei Deng and Olga Russakovsky · 2022
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Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent YF Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
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A survey on dataset distillation: Approaches, applications and future directions, 2023
Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong · 2023
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Towards understanding adversarial transferability in federated learning
Yijiang Li, Ying Gao, and Haohan Wang · 2023
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Energy-based sliced wasserstein distance
Khai Nguyen and Nhat Ho · 2023
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Data distillation: A survey, 2023
Noveen Sachdeva and Julian McAuley · 2023
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Loss-curvature matching for dataset selection and condensation, 2023
Seungjae Shin, Heesun Bae, Donghyeok Shin, Weonyoung Joo, and Il-Chul Moon · 2023
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Privacy for free: How does dataset condensation help privacy?
Tian Dong, Bo Zhao, and Lingjuan Lyu · 2022
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Sandwich batch normalization: A drop-in replacement for feature distribution heterogeneity
Xinyu Gong, Wuyang Chen, Tianlong Chen, and Zhangyang Wang · 2022
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Dataset condensation with latent space knowledge factorization and sharing
Hae Beom Lee, Dong Bok Lee, and Sung Ju Hwang · 2022
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A comprehensive survey of dataset distillation
Shiye Lei and Dacheng Tao · 2022
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More than encoder: Introducing transformer decoder to upsample
Yijiang Li, Wentian Cai, Ying Gao, Chengming Li, and Xiping Hu · 2022
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Dataset distillation via factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm, 2023
Peng Sun, Bei Shi, Daiwei Yu, and Tao Lin · 2023
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Dataset distillation in large data era, 2023
Zeyuan Yin and Zhiqiang Shen · 2023
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Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective, 2023
Zeyuan Yin, Eric Xing, and Zhiqiang Shen · 2023
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Dataset distillation: A comprehensive review, 2023
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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Improved distribution matching for dataset condensation, 2023
Ganlong Zhao, Guanbin Li, Yipeng Qin, and Yizhou Yu · 2023
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Mitigating bias in dataset distillation
Justin Cui, Ruochen Wang, Yuanhao Xiong, and Cho-Jui Hsieh · 2024
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A comprehensive survey of dataset distillation
Shiye Lei and Dacheng Tao · 2024
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Generalized large-scale data condensation via various backbone and statistical matching
Shitong Shao, Zeyuan Yin, Muxin Zhou, Xindong Zhang, and Zhiqiang Shen · 2024
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Towards adversarially robust dataset distillation by curvature regularization
Eric Xue, Yijiang Li, Haoyang Liu, Peiran Wang, Yifan Shen, and Haohan Wang · 2024
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M3d: Dataset condensation by minimizing maximum mean discrepancy, 2024
Hansong Zhang, Shikun Li, Pengju Wang, Dan Zeng, and Shiming Ge · 2024
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Self-supervised dataset distillation: A good compression is all you need
Muxin Zhou, Zeyuan Yin, Shitong Shao, and Zhiqiang Shen · 2024
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Towards adversarially robust dataset distillation by curvature regularization
Eric Xue, Yijiang Li, Haoyang Liu, Peiran Wang, Yifan Shen, and Haohan Wang · 2025
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