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Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan P. Adams · 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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Critical learning periods in deep neural networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Gradient descent happens in a tiny subspace
Guy Gur-Ari, Daniel A. Roberts, and Ethan Dyer · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Empirical analysis of the hessian of over-parametrized neural networks
Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, and Léon Bottou · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
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Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy M. Hospedales · 2020
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What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation
Jiahua Dong, Yang Cong, Gan Sun, Bineng Zhong, and Xiaowei Xu · 2020
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The early phase of neural network training
Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2020
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Federated learning via synthetic data
Jack Goetz and Ambuj Tewari · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Soft-label anonymous gastric x-ray image distillation
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2020
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Reducing catastrophic forgetting with learning on synthetic data
Wojciech Masarczyk and Ivona Tautkute · 2020
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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 Jeffrey Clune · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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On the generalization effects of linear transformations in data augmentation
Sen Wu, Hongyang R. Zhang, Gregory Valiant, and Christopher Ré · 2020
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2021
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Data distillation for text classification
Yongqi Li and Wenjie Li · 2021
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Diversity matters when learning from ensembles
Giung Nam, Jongmin Yoon, Yoonho Lee, and Juho Lee · 2021
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2021
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
Condensing graphs via one-step gradient matching
Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, and Bing Yin · 2022
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Graph condensation for graph neural networks
Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah · 2022
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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
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Dataset condensation with contrastive signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, and Sungroh Yoon · 2022
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Compressed gastric image generation based on soft-label dataset distillation for medical data sharing
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2022
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Distilled replay: Overcoming forgetting through synthetic samples
Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, and Davide Bacciu · 2021
Cited alongside, same era.
Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
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Time series data augmentation for deep learning: A survey
Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, and Huan Xu · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2022
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Graph condensation via receptive field distribution matching
Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song · 2022
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No one representation to rule them all: Overlapping features of training methods
Raphael Gontijo Lopes, Yann Dauphin, and Ekin Dogus Cubuk · 2022
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Improving ensemble distillation with weight averaging and diversifying perturbation
Giung Nam, Hyungi Lee, Byeongho Heo, and Juho Lee · 2022
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Sample condensation in online continual learning
Mattia Sangermano, Antonio Carta, Andrea Cossu, and Davide Bacciu · 2022
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When less is more: Simplifying inputs aids neural network understanding
Robin Tibor Schirrmeister, Rosanne Liu, Sara Hooker, and Tonio Ball · 2022
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Federated learning via decentralized dataset distillation in resource-constrained edge environments
Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, and Alois C. Knoll · 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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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt · 2022
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Feddm: Iterative distribution matching for communication-efficient federated learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2022
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Towards efficient data free black-box adversarial attack
Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, and Chao Wu · 2022
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Delving into the adversarial robustness of federated learning
Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, and Chao Wu · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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