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Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, Michael and Cohen, Neal J · 1989
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Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, Anthony · 1995
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Mnist handwritten digit database
LeCun, Yann, Cortes, Corinna, and Burges, CJ · 2010
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Dirichlet process
Teh, Yee Whye · 2010
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One shot learning of simple visual concepts
Lake, Brenden, Salakhutdinov, Ruslan, Gross, Jason, and Tenenbaum, Joshua · 2011
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Online incremental feature learning with denoising autoencoders
Zhou, Guanyu, Sohn, Kihyuk, and Lee, Honglak · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, Ian J, Mirza, Mehdi, Xiao, Da, Courville, Aaron, and Bengio, Yoshua · 2013
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
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Importance weighted autoencoders
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan · 2015
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The forget-me-not process
Milan, Kieran, Veness, Joel, Kirkpatrick, James, Bowling, Michael, Koop, Anna, and Hassabis, Demis · 2016
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Approximate inference for deep latent gaussian mixtures
Nalisnick, Eric, Hertel, Lars, and Smyth, Padhraic · 2016
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Rusu, Andrei A, Rabinowitz, Neil C, Desjardins, Guillaume, Soyer, Hubert, Kirkpatrick, James, Kavukcuoglu, Koray, Pascanu, Razvan, and Hadsell, Raia · 2016
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Neurogenesis deep learning: Extending deep networks to accommodate new classes
Draelos, Timothy J, Miner, Nadine E, Lamb, Christopher C, Cox, Jonathan A, Vineyard, Craig M, Carlson, Kristofor D, Severa, William M, James, Conrad D, and Aimone, James B · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Fernando, Chrisantha, Banarse, Dylan, Blundell, Charles, Zwols, Yori, Ha, David, Rusu, Andrei A, Pritzel, Alexander, and Wierstra, Daan · 2017
Cited alongside, same era.
Variational deep embedding: an unsupervised and generative approach to clustering
Jiang, Zhuxi, Zheng, Yin, Tan, Huachun, Tang, Bangsheng, and Zhou, Hanning · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, James, Pascanu, Razvan, Rabinowitz, Neil, Veness, Joel, Desjardins, Guillaume, Rusu, Andrei A, Milan, Kieran, Quan, John, Ramalho, Tiago, Grabska-Barwinska, Agnieszka, et al · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Lopez-Paz, David et al · 2017
Cited alongside, same era.
Stick-breaking variational autoencoders
Nalisnick, Eric and Smyth, Padhraic · 2017
Cited alongside, same era.
Overcoming catastrophic interference using conceptor-aided backpropagation
He, Xu and Jaeger, Herbert · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Hsu, Yen-Chang, Liu, Yen-Cheng, and Kira, Zsolt · 2018
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Learning without forgetting
Li, Zhizhong and Hoiem, Derek · 2018
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Learning to remember: Dynamic generative memory for continual learning
Ostapenko, Oleksiy, Puscas, Mihai, Klein, Tassilo, and Nabi, Moin · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, Jonathan, Czarnecki, Wojciech, Luketina, Jelena, Grabska-Barwinska, Agnieszka, Teh, Yee Whye, Pascanu, Razvan, and Hadsell, Raia · 2018
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Nguyen, Cuong V, Li, Yingzhen, Bui, Thang D, and Turner, Richard E · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Rebuffi, Sylvestre-Alvise, Kolesnikov, Alexander, Sperl, Georg, and Lampert, Christoph H · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Shin, Hanul, Lee, Jung Kwon, Kim, Jaehong, and Kim, Jiwon · 2017
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Yoon, Jaehong, Yang, Eunho, Lee, Jeongtae, and Hwang, Sung Ju · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, Friedemann, Poole, Ben, and Ganguli, Surya · 2017
Cited alongside, same era.
Life-long disentangled representation learning with cross-domain latent homologies
Achille, Alessandro, Eccles, Tom, Matthey, Loic, Burgess, Chris, Watters, Nicholas, Lerchner, Alexander, and Higgins, Irina · 2018
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget
Aljundi, Rahaf, Babiloni, Francesca, Elhoseiny, Mohamed, Rohrbach, Marcus, and Tuytelaars, Tinne · 2018
Cited alongside, same era.
Serra, Joan, Suris, Didac, Miron, Marius, and Karatzoglou, Alexandros · 2018
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Generative replay with feedback connections as a general strategy for continual learning
van de Ven, Gido M and Tolias, Andreas S · 2018
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Task agnostic continual learning using online variational bayes
Zeno, Chen, Golan, Itay, Hoffer, Elad, and Soudry, Daniel · 2018
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Task-free continual learning
Aljundi, Rahaf, Tuytelaars, Tinne, et al · 2019
Closest in time.
Continual learning via neural pruning
Golkar, Siavash, Kagan, Michael, and Cho, Kyunghyun · 2019
Closest in time.
Dirichlet variational autoencoder
Joo, Weonyoung, Lee, Wonsung, Park, Sungrae, and Moon, Il-Chul · 2019
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Continual lifelong learning with neural networks: A review
Parisi, German I, Kemker, Ronald, Part, Jose L, Kanan, Christopher, and Wermter, Stefan · 2019
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Unsupervised continual learning and self-taught associative memory hierarchies
Smith, James, Baer, Seth, Kira, Zsolt, and Dovrolis, Constantine · 2019
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Three scenarios for continual learning
van de Ven, Gido M and Tolias, Andreas S · 2019
Closest in time.