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Dataset distillation is the technique of synthesizing smaller condensed datasets from large original datasets while retaining necessary information to persist the effect.
Super-samples from kernel herding
Yutian Chen, Max Welling, and Alexander J. Smola · 2010
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 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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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
Earlier work this paper cites.
Data-free adversarial distillation
Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song · 2019
Cited alongside, same era.
Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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 · 2019
Cited alongside, same era.
Meta-learning with adaptive hyperparameters
Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, and Kyoung Mu Lee · 2020
Cited alongside, same era.
Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy M. Hospedales · 2020
Cited alongside, same era.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
Later among the works it cites.
Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
Later among the works it cites.
DC-BENCH: dataset condensation benchmark
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
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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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Cited alongside, same era.
Role-wise data augmentation for knowledge distillation
Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, and Hao Dong · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Revisiting knowledge distillation via label smoothing regularization
Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, and Jiashi Feng · 2020
Cited alongside, same era.
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, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2021
Cited alongside, same era.
Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia
Cited in the paper.
Closest in time.
Scaling up dataset distillation to imagenet-1k with constant memory
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 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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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2023
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Dataset distillation with convexified implicit gradients
Noel Loo, Ramin M. Hasani, Mathias Lechner, and Daniela Rus · 2023
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Squeeze, recover and relabel: Dataset condensation at imagenet scale from A new perspective
Zeyuan Yin, Eric P. Xing, and Zhiqiang Shen · 2023
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