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Dataset distillation aims to compress a dataset into a much smaller one so that a model trained on the distilled dataset achieves high accuracy.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Caltech-ucsd birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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A visually secure image encryption scheme based on compressive sensing
Xiuli Chai, Zhihua Gan, Yiran Chen, and Yushu Zhang · 2017
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Vorlesungen über Zahlentheorie
P.G.L. Dirichlet and R. Dedekind · 2017
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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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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
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Introduction to coresets: Accurate coresets
Ibrahim Jubran, Alaa Maalouf, and Dan Feldman · 2019
Cited alongside, same era.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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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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 T. F. Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
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Embarrassingly simple dataset distillation
Yunzhen Feng, Shanmukha Ramakrishna Vedantam, and Julia Kempe · 2023
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Cited alongside, same era.
Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
Cited alongside, same era.
Remember the past: Distilling datasets into addressable memories for neural networks
Zhiwei Deng and Olga Russakovsky · 2022
Cited alongside, same era.
Dataset condensation with latent space knowledge factorization and sharing
Hae Beom Lee, Dong Bok Lee, and Sung Ju Hwang
Cited in the paper.
Dataset condensation with contrastive signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, and Sungroh Yoon
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Dataset distillation with convexified implicit gradients
Noel Loo, Ramin Hasani, Mathias Lechner, and Daniela Rus · 2023
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Noveen Sachdeva and Julian McAuley · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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