Fetching the paper…
Reading the bibliography…
The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on the full, real dataset.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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 closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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 · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Earlier work this paper cites.
Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
Earlier work this paper cites.
Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales · 2020
Earlier work this paper cites.
Reducing catastrophic forgetting with learning on synthetic data
Wojciech Masarczyk and Ivona Tautkute · 2020
Earlier work this paper cites.
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
Earlier work this paper cites.
Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune · 2020
Earlier work this paper cites.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
Earlier work this paper cites.
Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
Cited alongside, same era.
Glister: Generalization based data subset selection for efficient and robust learning
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh Iyer · 2021
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.
Distilled replay: Overcoming forgetting through synthetic samples
Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, and Davide Bacciu · 2021
Cited alongside, same era.
Rethinking architecture selection in differentiable nas
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
Cited alongside, same era.
Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
Later among the works it cites.
Scaling up dataset distillation to imagenet-1k with constant memory
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2023
Closest in time.
Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent YF Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
Closest in time.
Dataset distillation with convexified implicit gradients
Noel Loo, Ramin Hasani, Mathias Lechner, and Daniela Rus · 2023
Closest in time.
Dim: Distilling dataset into generative model
Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, and Yang You · 2023
Closest in time.
Vision-language dataset distillation
Xindi Wu, Byron Zhang, Zhiwei Deng, and Olga Russakovsky · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Private set generation with discriminative information
Dingfan Chen, Raouf Kerkouche, and Mario Fritz · 2022
Cited alongside, same era.
Dc-bench: Dataset condensation benchmark
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Privacy for free: How does dataset condensation help privacy?
Tian Dong, Bo Zhao, and Lingjuan Lyu · 2022
Cited alongside, same era.
Graph condensation for graph neural networks
Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah · 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.
Closest in time.
Feddm: Iterative distribution matching for communication-efficient federated learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2023
Closest in time.
Does graph distillation see like vision dataset counterpart?
Beining Yang, Kai Wang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Hao Tang, Yang You, and Jianxin Li · 2023
Closest in time.
Dataset distillation: A comprehensive review
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
Closest in time.
Accelerating dataset distillation via model augmentation
Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, and Dongkuan Xu · 2023
Closest in time.
Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
Closest in time.
Improved distribution matching for dataset condensation
Ganlong Zhao, Guanbin Li, Yipeng Qin, and Yizhou Yu · 2023
Closest in time.
Daquan Zhou, Kai Wang, Jianyang Gu, Xiangyu Peng, Dongze Lian, Yifan Zhang, Yang You, and Jiashi Feng · 2023
Closest in time.
Better teacher better student: Dynamic prior knowledge for knowledge distillation
Martin Zong, Zengyu Qiu, Xinzhu Ma, Kunlin Yang, Chunya Liu, Jun Hou, Shuai Yi, and Wanli Ouyang · 2023
Closest in time.
Data distillation can be like vodka: Distilling more times for better quality
Xuxi Chen, Yu Yang, Zhangyang Wang, and Baharan Mirzasoleiman · 2024
Closest in time.