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Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J. Cohen · 1989
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Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Jürgen Schmidhuber · 1992
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
Moshe Leshno and Shimon Schocken · 1993
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Catastrophic Forgetting, Rehearsal and Pseudorehearsal
Anthony Robins · 1995
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Catastrophic forgetting in connectionist networks
Robert M. French · 1999
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Neuromodulation of Neuronal Circuits: Back to the Future
Eve Marder · 2012
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Context-dependent computation by recurrent dynamics in prefrontal cortex
Valerio Mante, David Sussillo, Krishna V. Shenoy, and William T. Newsome · 2013
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A Normative Theory of Forgetting: Lessons from the Fruit Fly
Johanni Brea, Robert Urbanczik, and Walter Senn · 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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Controlling Recurrent Neural Networks by Conceptors
Herbert Jaeger · 2014
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Diederik P. Kingma and Max Welling · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Learning feed-forward one-shot learners
Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, and Andrea Vedaldi · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Dynamic Filter Networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool · 2016
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What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
Dharshan Kumaran, Demis Hassabis, and James L. McClelland · 2016
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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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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
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HyperNetworks
David Ha, Andrew M. Dai, and Quoc V. Le · 2017
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Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Boris Hanin · 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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Nicolas Y Masse, Gregory D Grant, and David J Freedman · 2018
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P Lillicrap, and Greg Wayne · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2017
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Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos and Max Welling · 2017
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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
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Implicit Weight Uncertainty in Neural Networks
Nick Pawlowski, Andrew Brock, Matthew C. H. Lee, Martin Rajchl, and Ben Glocker · 2017
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The Persistence and Transience of Memory
Blake A. Richards and Paul W. Frankland · 2017
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Continual Learning with Deep Generative Replay
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Continual Learning Through Synaptic Intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Overcoming catastrophic forgetting with hard attention to the task
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Motor primitives in space and time via targeted gain modulation in cortical networks
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Improving and understanding variational continual learning
Siddharth Swaroop, Cuong V Nguyen, Thang D Bui, and Richard E Turner · 2018
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Generative replay with feedback connections as a general strategy for continual learning
Gido M. van de Ven and Andreas S. Tolias · 2018
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Memory Replay GANs: Learning to Generate New Categories without Forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, yaxing wang, Joost van de Weijer, and Bogdan Raducanu · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Large scale adversarial representation learning
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Task agnostic continual learning via meta learning
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Continual lifelong learning with neural networks: A review
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Three scenarios for continual learning
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