Fetching the paper…
Reading the bibliography…
In this study, we propose a novel dataset distillation method based on parameter pruning.
“Super-samples from kernel herding,”
Yutian Chen, Max Welling, and Alex Smola, · 2010
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
“Very deep convolutional networks for large-scale image recognition,”
Karen Simonyan and Andrew Zisserman, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“A survey of deep neural network architectures and their applications,”
Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, and Fuad E Alsaadi, · 2017
Earlier work this paper cites.
“Practical coreset constructions for machine learning,”
Olivier Bachem, Mario Lucic, and Andreas Krause, · 2017
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros, · 2018
Earlier work this paper cites.
“Dynamic few-shot visual learning without forgetting,”
Spyros Gidaris and Nikos Komodakis, · 2018
Earlier work this paper cites.
“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.
“Soft-label anonymous gastric x-ray image distillation,”
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama, · 2020
Cited alongside, same era.
“Flexible dataset distillation: Learn labels instead of images,”
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales, · 2020
Cited alongside, same era.
“Condensed composite memory continual learning,”
Felix Wiewel and Bin Yang, · 2021
Cited alongside, same era.
“Dataset condensation with gradient matching,”
Bo Zhao and Hakan Bilen, · 2021
Cited alongside, same era.
“Dataset condensation with differentiable siamese augmentation,”
Bo Zhao and Hakan Bilen, · 2021
Cited alongside, same era.
“Privacy for free: How does dataset condensation help privacy?,”
Tian Dong, Bo Zhao, and Lingjuan Liu, · 2022
Closest in time.
“Compressed gastric image generation based on soft-label dataset distillation for medical data sharing,”
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama, · 2022
Closest in time.
“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
Closest in time.
“Dataset distillation by matching training trajectories,”
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu, · 2022
Closest in time.
“Dataset distillation for medical dataset sharing,”
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama, · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Dataset distillation with infinitely wide convolutional networks,”
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee, · 2021
Cited alongside, same era.
“Ld-net: A lightweight network for real-time self-supervised monocular depth estimation,”
Mingkang Xiong, Zhenghong Zhang, Tao Zhang, and Huilin Xiong, · 2022
Cited alongside, same era.
Ruonan Yu, Songhua Liu, and Xinchao Wang, · 2023
Closest in time.
“Dataset condensation with distribution matching,”
Bo Zhao and Hakan Bilen, · 2023
Closest in time.