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Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the training of accurate neural models.
Two-person cooperative games
John Nash · 1953
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Morse-sard theorem for real-analytic functions
Jiří Souček and Vladimír Souček · 1972
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Large deviations , volume 342
Jean-Dominique Deuschel and Daniel W Stroock · 2001
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Large deviations bounds for estimating conditional value-at-risk
David B Brown · 2007
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer Yuval · 2011
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Convolutional neural networks applied to house numbers digit classification
Pierre Sermanet, Soumith Chintala, and Yann LeCun · 2012
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Robustness and generalization
Huan Xu and Shie Mannor · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Distributionally robust language modeling
Yonatan Oren, Shiori Sagawa, Tatsunori B Hashimoto, and Percy Liang · 2019
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Distributionally robust neural networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 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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Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang · 2020
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Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds
Mohsen Joneidi, Saeed Vahidian, Ashkan Esmaeili, Weijia Wang, Nazanin Rahnavard, Bill Lin, and Mubarak Shah · 2020
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Pac-bayesian bound for the conditional value at risk
Zakaria Mhammedi, Benjamin Guedj, and Robert C Williamson · 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
Cited alongside, same era.
Coresets for estimating means and mean square error with limited greedy samples
Saeed Vahidian, Baharan Mirzasoleiman, and Alexander Cloninger · 2020
Cited alongside, same era.
Generalization bounds with minimal dependency on hypothesis class via distributionally robust optimization
Yibo Zeng and Henry Lam · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
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Generalizing dataset distillation via deep generative prior
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2023
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Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
Jiawei Du, Yidi Jiang, Vincent Y.F. Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
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Yuqi Jia, Saeed Vahidian, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, and Yiran Chen · 2023
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On robustness and transferability of convolutional neural networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, et al · 2021
Cited alongside, same era.
Learning models with uniform performance via distributionally robust optimization
John C Duchi and Hongseok Namkoong · 2021
Cited alongside, same era.
Learning to generate synthetic training data using gradient matching and implicit differentiation
Dmitry Medvedev and Alexander D’yakonov · 2021
Cited alongside, same era.
Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
Cited alongside, same era.
From data to decisions: Distributionally robust optimization is optimal
Bart PG Van Parys, Peyman Mohajerin Esfahani, and Daniel Kuhn · 2021
Cited alongside, same era.
Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Yang Jiao, Kai Yang, and Dongjin Song · 2023
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Dream: Efficient dataset distillation by representative matching
Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Zhu, Wei Jiang, and Yang You · 2023
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Dataset distillation with convexified implicit gradients
Noel Loo, Ramin Hasani, Mathias Lechner, and Daniela Rus · 2023
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Data distillation: A survey
Noveen Sachdeva and Julian McAuley · 2023
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DataDAM: Efficient Dataset Distillation with Attention Matching
Ahmad Sajedi, Samir Khaki, Ehsan Amjadian, Lucy Z Liu, Yuri A Lawryshyn, and Konstantinos N Plataniotis · 2023
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Group distributionally robust knowledge distillation
Konstantinos Vilouras, Xiao Liu, Pedro Sanchez, Alison Q O’Neil, and Sotirios A Tsaftaris · 2023
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Multimodal dataset distillation for image-text retrieval
Xindi Wu, Zhiwei Deng, and Olga Russakovsky · 2023
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FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2023
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Change is hard: A closer look at subpopulation shift
Yuzhe Yang, Haoran Zhang, Dina Katabi, and Marzyeh Ghassemi · 2023
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Improved Distribution Matching for Dataset Condensation
Ganlong Zhao, Guanbin Li, Yipeng Qin, and Yizhou Yu · 2023
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Hypo: Hyperspherical out-of-distribution generalization
Haoyue Bai, Yifei Ming, Julian Katz-Samuels, and Yixuan Li · 2024
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Data distillation can be like vodka: Distilling more times for better quality
Xuxi Chen, Yu Yang, Zhangyang Wang, and Baharan Mirzasoleiman · 2024
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Sparse parameterization for epitomic dataset distillation
Xing Wei, Anjia Cao, Funing Yang, and Zhiheng Ma · 2024
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Towards adversarially robust dataset distillation by curvature regularization
Eric Xue, Yijiang Li, Haoyang Liu, Yifan Shen, and Haohan Wang · 2024
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