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Dataset distillation is a newly emerging task that synthesizes a small-size dataset used in training deep neural networks (DNNs) for reducing data storage and model training costs.
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Sariel Har-Peled and Akash Kushal · 2005
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Core vector machines: Fast svm training on very large data sets
Ivor W Tsang, James T Kwok, Pak-Ming Cheung, and Nello Cristianini · 2005
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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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CIFAR-10 and CIFAR-100 datasets
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Super-samples from kernel herding
Yutian Chen, Max Welling, and Alex Smola · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Matthew D Zeiler and Rob Fergus · 2014
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Tiny imagenet visual recognition challenge
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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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Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent TF Tan, Joey Tianyi Zhou, and Haizhou Li · 2022
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Delving into effective gradient matching for dataset condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu, and David Z Pan · 2022
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Dataset condensation via efficient synthetic-data parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song · 2022
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Dataset condensation via efficient synthetic-data parameterization
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Soft-label anonymous gastric x-ray image distillation
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Dataset meta-learning from kernel ridge-regression
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Dataset distillation with infinitely wide convolutional networks
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A comprehensive survey of neural architecture search: Challenges and solutions
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Distilled replay: Overcoming forgetting through synthetic samples
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Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song · 2022
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Dataset condensation with latent space knowledge factorization and sharing
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Meta knowledge condensation for federated learning
Ping Liu, Xin Yu, and Joey Tianyi Zhou · 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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Efficient dataset distillation using random feature approximation
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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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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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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A comprehensive survey to dataset distillation
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Dataset distillation: A comprehensive review
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Accelerating dataset distillation via model augmentation
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Dataset condensation with distribution matching
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