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Dataset distillation (DD) is a newly emerging research area aiming at alleviating the heavy computational load in training models on large datasets.
Model compression
Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, K. Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Herding dynamical weights to learn
Max Welling · 2009
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Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering
Dan Feldman, Melanie Schmidt, and Christian Sohler · 2013
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Do deep nets really need to be deep?
Lei Jimmy Ba and Rich Caruana · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
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Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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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
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Contextual diversity for active learning
Sharat Agarwal, Himanshu Arora, Saket Anand, and Chetan Arora · 2020
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Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
Cited alongside, same era.
Submodular combinatorial information measures with applications in machine learning
Rishabh Iyer, Ninad Khargonkar, Jeff Bilmes, and Himanshu Asnani · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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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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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A survey on dataset distillation: Approaches, applications and future directions
Zongxion Geng, Jiahui andg Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong · 2023
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A comprehensive survey to dataset distillation
Shiye Lei and Dacheng Tao · 2023
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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 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
Cited alongside, same era.
Dataset distillation via factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
Cited alongside, same era.
Efficient dataset distillation using random feature approximation
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 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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Data distillation: A survey
Noveen Sachdeva and Julian McAuley · 2023
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A comprehensive survey to dataset distillation
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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Accelerating dataset distillation via model augmentation
Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, and Xu Dongkuan · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 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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Dataset quantization
Daquan Zhou, Kai Wang, Jianyang Gu, Xiangyu Peng, Dongze Lian, Yifan Zhang, Yang You, and Jiashi Feng · 2023
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Rethinking data distillation: Do not overlook calibration
Dongyao Zhu, Bowen Lei, Jie Zhang, Yanbo Fang, Ruqi Zhang, Yiqun Xie, and Dongkuan Xu · 2023
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