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Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn · 2005
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Differential privacy
Cynthia Dwork · 2006
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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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 2017
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Learning active learning from data
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 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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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Image2stylegan: How to embed images into the stylegan latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 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 Stanley, and Jeffrey Clune · 2020
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Towards general deep leakage in federated learning
Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker, and Chunming Rong · 2021
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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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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 latent space knowledge factorization and sharing
Hae Beom Lee, Dong Bok Lee, and Sung Ju Hwang · 2022
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Dataset distillation for medical dataset sharing
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2022
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Graph condensation via receptive field distribution matching
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Graph condensation for graph neural networks
Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah · 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.
Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
Cited alongside, same era.
Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Bilen Hakan Zhao Bo · 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.
Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song · 2022
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Dataset distillation via factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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Efficient dataset distillation using random feature approximation
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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Can we achieve robustness from data alone?
Nikolaos Tsilivis, Jingtong Su, and Julia Kempe · 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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Towards robust dataset learning
Yihan Wu, Xinda Li, Florian Kerschbaum, Heng Huang, and Hongyang Zhang · 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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Synthesizing informative training samples with gan
Bo Zhao and Hakan Bilen · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
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Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Improved gradient inversion attacks and defenses in federated learning
Jiahui Geng, Yongli Mou, Qing Li, Feifei Li, Oya Beyan, Stefan Decker, and Chunming Rong · 2023
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Backdoor attacks against dataset distillation
Yugeng Liu, Zheng Li, Michael Backes, Yun Shen, and Yang Zhang · 2023
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