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Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks.
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S. and Pitts, W · 1943
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Catastrophic interference in connectionist networks: the sequential learning problem
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Optimal brain damage
LeCun, Y., Denker, J. S., and Solla, S. A · 1990
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Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Ratcliff, R · 1990
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Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks
French, R. M · 1991
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Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, A · 1995
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Lifelong robot learning
Thrun, S. and Mitchell, T · 1995
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Sequence learning
Clegg, B. A., DiGirolamo, G. J., and Keele, S. W · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Improved multitask learning through synaptic intelligence
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Task clustering and gating for bayesian multitask learning
Bakker, B. and Heskes, T · 2003
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Regularized multi-task learning
Evgeniou, T. and Pontil, M · 2004
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Universal intelligence: a definition of machine intelligence
Legg, S. and Hutter, M · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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NotMNIST dataset
Bulatov, Y · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A · 2011
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The German traffic sign recognition benchmark: a multi-class classification competition
Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C · 2011
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
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Compete to compute
Srivastava, R. K., Masci, J., Kazerounian, S., Gomez, F., and Schmidhuber, J · 2013
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Lee, S.-W., Kim, J.-H., Jun, J., Ha, J.-W., and Zhang, B.-T · 2017
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Learning without forgetting
Li, Z. and Hoiem, D · 2017
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Gradient episodic memory for continuum learning
Lopez-Paz, D. and Ranzato, M. A · 2017
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PackNet: adding multiple tasks to a single network by iterative pruning
Mallya, A. and Lazebnik, S · 2017
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Variational continual learning
Nguyen, C., Li, Y., Bui, T. D., and Turner, R. E · 2017
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I., Mizra, M., Da, X., Courville, A., and Bengio, Y · 2014
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A data-driven approach to cleaning large face datasets
Ng, H.-W. and Winkler, S · 2014
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Reduction of catastrophic forgetting with transfer learning and ternary output codes
Gutsein, S. and Stump, E · 2015
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Adam: a method for stochastic optimization
Kingma, D. P. and Ba, J. L · 2015
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Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
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Less-forgetting learning in deep neural networks
Jung, H., Ju, J., Jung, M., and Kim, J · 2016
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Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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iCaRL: incremental classifier and representation learning
Rebuffi, S., Kolesnikov, A., Sperl, G., and Lampert, C · 2017
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Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
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A strategy for an uncompromising incremental learner
Venkatesan, R., Venkateswara, H., Panchanathan, S., and Li, B · 2017
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On the origin of deep learning
Wang, H. and Raj, B · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Overcoming catastrophic interference using conceptor-aided backpropagation
He, X. and Jaeger, H · 2018
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Memory-based parameter adaptation
Sprechmann, P., Jayakumar, S., Rae, J., Pritzel, A., Puigdomènech, A., Uria, B., Vinyals, O., Hassabis, D., Pascanu, R., and Blundell, C · 2018
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Lifelong learning with dynamically expandable networks
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
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