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Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner.
A case study of incremental concept induction
J. Schlimmer and D. Fisher · 1986
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A massively parallel architecture for a self-organizing neural pattern recognition machine
G. Carpenter and S. Grossberg · 1987
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Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. Cohen · 1989
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Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions
R. Ratcliff · 1990
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Catastrophic forgetting in neural networks: The role of rehearsal mechanisms
A. Robins · 1993
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Online learning with random representations
R. Sutton and S. Whitehead · 1993
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Continual learning in reinforcement environments
M. Ring · 1995
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Catastrophic forgetting, rehearsal and pseudorehearsal
A. Robins · 1995
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Explanation-based neural network learning: A lifelong learning approach
S. Thrun · 1996
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Multi-task learning
R. Caruana · 1997
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CHILD: A first step towards continual learning
M. Ring · 1997
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Catastrophic forgetting in connectionist networks
R. French · 1999
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Bayesian theory
J. Bernardo and A. Smith · 2000
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Empirical Bayes for learning to learn
T. Heskes · 2000
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Task clustering and gating for Bayesian multitask learning
B. Bakker and T. Heskes · 2003
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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The notMNIST dataset
Y. Butalov · 2011
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One shot learning of simple visual concepts
B. Lake, R. Salakhutdinov, J. Gross, and J. Tenenbaum · 2011
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Modular deep belief networks that do not forget
L. Pape, F. Gomez, M. Ring, and J. Schmidhuber · 2011
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Maxout networks
I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Powerplay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem
J. Schmidhuber · 2013
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Compete to compute
R. Srivastava, J. Masci, S. Kazerounian, F. Gomez, and J. Schmidhuber · 2013
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A biologically inspired dual-network memory model for reduction of catastrophic forgetting
M. Hattori · 2014
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Semi-supervised learning with deep generative models
D. Kingma, D. Rezende, S. Mohamed, and M. Welling · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models
P. Swietojanski and S. Renals · 2014
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Adam: A Method for Stochastic Optimization
D. Kingma and J. Ba · 2015
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Variational dropout and the local reparameterization trick
D. Kingma, T. Salimans, and M. Welling · 2015
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NIPS 2016 tutorial: Generative adversarial networks
I. Goodfellow · 2016
Cited alongside, same era.
Less-forgetting learning in deep neural networks
H. Jung, J. Ju, M. Jung, and J. Kim · 2016
Cited alongside, same era.
Learning without forgetting
Z. Li and D. Hoiem · 2016
Cited alongside, same era.
Online contrastive divergence with generative replay: Experience replay without storing data
D. Mocanu, M. Vega, E. Eaton, P. Stone, and A. Liotta · 2016
Cited alongside, same era.
Unsupervised domain adaptation with a relaxed covariate shift assumption
T. Adel, H. Zhao, and A. Wong · 2017
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J. Schmidhuber · 2018
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Progress & compress: A scalable framework for continual learning
J. Schwarz, J. Luketina, W. Czarnecki, A. Grabska-Barwinska, Y. Whye Teh, R. Pascanu, and R. Hadsell · 2018
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Overcoming catastrophic forgetting with hard attention to the task
J. Serra, D. Suris, M. Miron, and A. Karatzoglou · 2018
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Generative replay with feedback connections as a general strategy for continual learning
G. van de Ven and A. Tolias · 2018
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R. Vuorio, D. Cho, D. Kim, and J. Kim · 2018
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C. Fernando, D. Banarse, C. Blundell, Y. Zwols, D. Ha, A. Rusu, A. Pritzel, and D. Wierstra · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Deep generative dual memory network for continual learning
N. Kamra, U. Gupta, and Y. Liu · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting by incremental moment matching
S. Lee, J. Kim, J. Jun, J. Ha, and B. Zhang · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
D. Lopez-Paz and M. Ranzato · 2017
Cited alongside, same era.
Bayesian compression for deep learning
C. Louizos, K. Ullrich, and M. Welling · 2017
Cited alongside, same era.
Memory replay GANs: Learning to generate new categories without forgetting
C. Wu, L. Herranz, X. Liu, Y. Wang, J. van de Weijer, and B. Raducanu · 2018
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Reinforced continual learning
J. Xu and Z. Zhu · 2018
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Lifelong learning with dynamically expandable networks
J. Yoon, E. Yang, J. Lee, and S. Hwang · 2018
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Task agnostic continual learning using online variational Bayes
C. Zeno, I. Golan, E. Hoffer, and D. Soudry · 2018
Later among the works it cites.
Uncertainty-based continual learning with adaptive regularization
H. Ahn, D. Lee, S. Cha, and T. Moon · 2019
Closest in time.
Autoencoder-based incremental class learning without retraining on old data
E. Choi, K. Lee, and K. Choi · 2019
Closest in time.
Single-net continual learning with progressive segmented training (PST)
X. Du, G. Charan, F. Liu, and Y. Cao · 2019
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Uncertainty-guided continual learning with Bayesian neural networks
S. Ebrahimi, M. Elhoseiny, T. Darrell, and M. Rohrbach · 2019
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Task agnostic continual learning via meta learning
X. He, J. Sygnowski, A. Galashov, A. Rusu, Y. Whye Teh, and R. Pascanu · 2019
Closest in time.
Overcoming catastrophic forgetting via model adaptation
W. Hu, Z. Lin, B. Liu, C. Tao, Z. Tao, J. Ma, D. Zhao, and R. Yan · 2019
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Meta-learning representations for continual learning
K. Javed and M. White · 2019
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Incremental learning with maximum entropy regularization: Rethinking forgetting and intransigence
D. Kim, J. Bae, Y. Jo, and J. Choi · 2019
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Learning to remember: A synaptic plasticity driven framework for continual learning
O. Ostapenko, M. Puscas, T. Klein, P. Jahnichen, and M. Nabi · 2019
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Continual lifelong learning with neural networks: A review
G. Parisi, R. Kemker, J. Part, C. Kanan, and S. Wermter · 2019
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Continual learning by asymmetric loss approximation with single-side overestimation
D. Park, S. Hong, B. Han, and K. Lee · 2019
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A comprehensive, application-oriented study of catastrophic forgetting in DNNs
B. Pfulb and A. Gepperth · 2019
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Random path selection for incremental learning
J. Rajasegaran, M. Hayat, S. Khan, F. Khan, and L. Shao · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
M. Riemer, I. Cases, R. Ajemian, M. Liu, I.Rish, Y. Tu, and G. Tesauro · 2019
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BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning
A. Stickland and I. Murray · 2019
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Continual learning via online leverage score sampling
D. Teng and S. Dasgupta · 2019
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Functional regularisation for continual learning using Gaussian processes
M. Titsias, J. Schwarz, A. Matthews, R. Pascanu, and Y. Whye Teh · 2019
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Bayesian Optimized Continual Learning with Attention Mechanism
J. Xu, J. Ma, and Z. Zhu · 2019
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ORACLE: Order robust adaptive continual learning
J. Yoon, S. Kim, E. Yang, and S. Hwang · 2019
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