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Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally.
McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of Learning and Motivation 24
1989
Earlier work this paper cites.
Ratcliff, R.: Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychological review 97
1990
Earlier work this paper cites.
French, R.M.: Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference. In: Cognitive Science Society Conf. (1994)
1994
Earlier work this paper cites.
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20
1995
Earlier work this paper cites.
Thrun, S.: Lifelong Learning Algorithms, pp. 181–209. Springer US (1998)
1998
Earlier work this paper cites.
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: NIPS (2000)
2000
Earlier work this paper cites.
Ruping, S.: Incremental learning with support vector machines. In: ICDM (2001)
2001
Earlier work this paper cites.
Ans, B., Rousset, S., French, R.M., Musca, S.: Self-refreshing memory in artificial neural networks: Learning temporal sequences without catastrophic forgetting. Connection Science 16
2004
Earlier work this paper cites.
Krizhevsky, A.: Learning multiple layers of features from tiny images. Tech. rep., University of Toronto (2009)
2009
Earlier work this paper cites.
Welling, M.: Herding dynamical weights to learn. In: ICML (2009)
2009
Earlier work this paper cites.
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. PAMI 35
2013
Earlier work this paper cites.
Chen, X., Shrivastava, A., Gupta, A.: NEIL: Extracting visual knowledge from web data. In: ICCV (2013)
2013
Earlier work this paper cites.
Goodfellow, I., Mirza, M., Xiao, D., Courville, A., Bengio, Y.: An empirical investigation of catastrophic forgetting in gradient-based neural networks. ArXiv e-prints, arXiv 1312.6211 (2013)
2013
Cited alongside, same era.
Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Distance-based image classification: Generalizing to new classes at near-zero cost. PAMI 35
2013
Cited alongside, same era.
Ruvolo, P., Eaton, E.: ELLA: An efficient lifelong learning algorithm. In: ICML (2013)
2013
Cited alongside, same era.
Divvala, S., Farhadi, A., Guestrin, C.: Learning everything about anything: Webly-supervised visual concept learning. In: CVPR (2014)
2014
Cited alongside, same era.
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS workshop (2014)
2014
Cited alongside, same era.
Vedaldi, A., Lenc, K.: MatConvNet – Convolutional Neural Networks for MATLAB. In: ACM Multimedia (2015)
2015
Later among the works it cites.
Furlanello, T., Zhao, J., Saxe, A.M., Itti, L., Tjan, B.S.: Active long term memory networks. ArXiv e-prints, arXiv 1606.02355 (2016)
2016
Later among the works it cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Later among the works it cites.
Jung, H., Ju, J., Jung, M., Kim, J.: Less-forgetting learning in deep neural networks. ArXiv e-prints, arXiv 1607.00122 (2016)
2016
Later among the works it cites.
Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. ArXiv e-prints, arXiv 1606.04671 (2016)
2016
Later among the works it cites.
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Ristin, M., Guillaumin, M., Gall, J., Gool, L.V.: Incremental learning of ncm forests for large-scale image classification. In: CVPR (2014)
2014
Cited alongside, same era.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS (2014)
2014
Cited alongside, same era.
Xiao, T., Zhang, J., Yang, K., Peng, Y., Zhang, Z.: Error-driven incremental learning in deep convolutional neural network for large-scale image classification. In: ACM Multimedia (2014)
2014
Cited alongside, same era.
Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Mishra, B.D., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E., Ritter, A., Samadi, M., Settles, B., Wang, R., Wijaya, D., Gupta, A., Chen, X., Saparov, A., Greaves, M., Welling, J.: Never-ending learning. In: AAAI (2015)
2015
Cited alongside, same era.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS (2015)
2015
Cited alongside, same era.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV 115
2015
Cited alongside, same era.
Terekhov, A.V., Montone, G., O’Regan, J.K.: Knowledge transfer in deep block-modular neural networks. In: Biomimetic and Biohybrid Systems (2015)
2015
Cited alongside, same era.
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., Hadsell, R.: Overcoming catastrophic forgetting in neural networks. Proc. National Academy of Sciences 114
2017
Later among the works it cites.
Lopez-Paz, D., Ranzato, M.A.: Gradient episodic memory for continual learning. In: NIPS (2017)
2017
Later among the works it cites.
Neelakantan, A., Vilnis, L., Le, Q.V., Sutskever, I., Kaiser, L., Kurach, K., Martens, J.: Adding gradient noise improves learning for very deep networks. ArXiv e-prints, arXiv 1511.06807 (2017)
2017
Later among the works it cites.
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: Incremental classifier and representation learning. In: CVPR (2017)
2017
Later among the works it cites.
Shmelkov, K., Schmid, C., Alahari, K.: Incremental learning of object detectors without catastrophic forgetting. In: ICCV (2017)
2017
Later among the works it cites.
Triki, A.R., Aljundi, R., Blaschko, M.B., Tuytelaars, T.: Encoder based lifelong learning. In: ICCV (2017)
2017
Later among the works it cites.
Li, Z., Hoiem, D.: Learning without forgetting. PAMI (2018)
2018
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