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Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Core vector machines: Fast svm training on very large data sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
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Smaller coresets for k-median and k-means clustering
Sariel Har-Peled and Akash Kushal · 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 and Geoffrey Hinton · 2009
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Super-samples from kernel herding
Yutian Chen, Max Welling, and Alex Smola · 2010
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A review of instance selection methods
J Arturo Olvera-López, J Ariel Carrasco-Ochoa, J Francisco Martínez-Trinidad, and Josef Kittler · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Are all training examples equally valuable?
Agata Lapedriza, Hamed Pirsiavash, Zoya Bylinskii, and Antonio Torralba · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 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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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, and Jan Peters · 2018
Cited alongside, same era.
Reducing catastrophic forgetting with learning on synthetic data
Wojciech Masarczyk and Ivona Tautkute · 2020
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
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Kornia: an open source differentiable computer vision library for pytorch
E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski · 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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Secdd: Efficient and secure method for remotely training neural networks
Ilia Sucholutsky and Matthias Schonlau · 2020
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Cited alongside, same era.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Cited alongside, same era.
Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales · 2020
Cited alongside, same era.
Coresets via bilevel optimization for continual learning and streaming
Zalán Borsos, Mojmir Mutny, and Andreas Krause · 2020
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Towards good practices for data augmentation in gan training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung · 2020
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
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Image augmentations for gan training
Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang · 2020
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Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
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Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2021
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Cafe: Learning to condense dataset by aligning features
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You · 2022
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