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We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
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Compression in visual working memory: using statistical regularities to form more efficient memory representations
Timothy F Brady, Talia Konkle, and George A Alvarez · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects
Martial Mermillod, Aurélia Bugaiska, and Patrick Bonin · 2013
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Human associative memory
John R Anderson and Gordon H Bower · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Forward and reverse gradient-based hyperparameter optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
La-maml: Look-ahead meta learning for continual learning
Gunshi Gupta, Karmesh Yadav, and Liam Paull · 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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Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
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Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2020
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Dataset condensation with gradient matching
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Human memory: The processing of information
Geoffrey R Loftus and Elizabeth F Loftus · 2019
Cited alongside, same era.
Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2019
Cited alongside, same era.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
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Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon 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 condensation with distribution matching
Bo Zhao and Hakan Bilen · 2021
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Pragmatic image compression for human-in-the-loop decision-making
Sid Reddy, Anca Dragan, and Sergey Levine · 2021
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Comparing distributions by measuring differences that affect decision making
Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, and Stefano Ermon · 2021
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Learning where to learn: Gradient sparsity in meta and continual learning
Johannes Von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, and João Sacramento · 2021
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Kernel continual learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, and Cees Snoek · 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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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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