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Classical machine learning algorithms often assume that the data are drawn i.i.d.
Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 1904
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 1905
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Task agnostic continual learning via meta learning
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, and Razvan Pascanu · 1906
Earlier work this paper cites.
Discorl: Continual reinforcement learning via policy distillation
René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz Rodríguez, and David Filliat · 1907
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Online learned continual compression with adaptative quantization module
Lucas Caccia, Eugene Belilovsky, Massimo Caccia, and Joelle Pineau · 1911
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Regularization shortcomings for continual learning
Timothée Lesort, Andrei Stoian, and David Filliat · 1912
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Discriminability-based transfer between neural networks
Lorien Y Pratt · 1993
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Lifelong robot learning
Sebastian Thrun and Tom M Mitchell · 1995
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Catastrophic forgetting in connectionist networks
Robert M. French · 1999
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The impact of changing populations on classifier performance
Mark G Kelly, David J Hand, and Niall M Adams · 1999
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Online fast adaptation and knowledge accumulation: a new approach to continual learning
Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, and Laurent Charlin · 2003
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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Insights from the Future for Continual Learning
Arthur Douillard, Eduardo Valle, Charles Ollion, Thomas Robert, and Matthieu Cord · 2006
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Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes
Timothée Lesort · 2007
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Metalearning: Applications to Data Mining
Pavel Brazdil, Christophe Giraud-Carrier, Carlos Soares, and Ricardo Vilalta · 2008
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Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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Martin Mundt, Yong Won Hong, Iuliia Pliushch, and Visvanathan Ramesh · 2009
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Learning under concept drift: an overview
Indrė Žliobaitė · 2010
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Toward an architecture for never-ending language learning
Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka Jr, and Tom M Mitchell · 2010
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One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 2011
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A unifying view on dataset shift in classification
Jose G. Moreno-Torres, Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V. Chawla, and Francisco Herrera · 2011
Earlier work this paper cites.
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Masashi Sugiyama and Motoaki Kawanabe · 2012
Cited alongside, same era.
A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
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Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Never-ending learning
T. Mitchell, W. Cohen, E. Hruscha, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohammad, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, and J. Welling · 2015
Cited alongside, same era.
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
Cited alongside, same era.
Online learning: A comprehensive survey
Steven CH Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao · 2018
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Risto Vuorio, Dong-Yeon Cho, Daejoong Kim, and Jiwon Kim · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
Cited alongside, same era.
Incremental learning algorithms and applications
Alexander Gepperth and Barbara Hammer · 2016
Cited alongside, same era.
Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, and Daan Wierstra · 2017
Cited alongside, same era.
Zhizhong Li and Derek Hoiem · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedeman Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Tensor based knowledge transfer across skill categories for robot control
Chenyang Zhao, Timothy M Hospedales, Freek Stulp, and Olivier Sigaud · 2017
Cited alongside, same era.
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
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Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, and Natalia Díaz-Rodríguez · 2019
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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 · 2019
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Meta-learning representations for continual learning
Khurram Javed and Martha White · 2019
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Optimal continual learning has perfect memory and is np-hard
Jeremias Knoblauch, Hisham Husain, and Tom Diethe · 2020
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Invariant risk minimization games, 2020
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, and Amit Dhurandhar · 2020
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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Dive into Deep Learning
Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola · 2020
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A closer look at invalid action masking in policy gradient algorithms, 2020
Shengyi Huang and Santiago Ontañón · 2020
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Modeling the background for incremental learning in semantic segmentation
Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, and Barbara Caputo · 2020
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itaml: An incremental task-agnostic meta-learning approach
Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Mubarak Shah · 2020
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Incremental few-shot meta-learning via indirect discriminant alignment
Qing Liu, Orchid Majumder, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto · 2020
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Out-of-distribution generalization via risk extrapolation (rex), 2021
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Continual prototype evolution: Learning online from non-stationary data streams
Matthias De Lange and Tinne Tuytelaars · 2021
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Iirc: Incremental implicitly-refined classification
Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, and Sarath Chandar · 2021
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Continuum: Simple management of complex continual learning scenarios
Arthur Douillard and Timothée Lesort · 2021
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