Distillation Techniques for Pseudo-rehearsal Based Incremental Learning

McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of learning and motivation, vol. 24, pp. 109–165. Elsevier (1989)

1989

Earlier work this paper cites.

Robins, A.: Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Science 7(2), 123–146 (1995)

1995

Earlier work this paper cites.

Bucilua, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 535–541. ACM (2006)

2006

Earlier work this paper cites.

LeCun, Y., Cortes, C., Burges, C.: Mnist handwritten digit database. AT&T Labs [Online]. Available: http://yann. lecun. com/exdb/mnist 2 (2010)

2010

Earlier work this paper cites.

Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Distance-based image classification: Generalizing to new classes at near-zero cost. IEEE transactions on pattern analysis and machine intelligence 35(11), 2624–2637 (2013)

2013

Earlier work this paper cites.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)

2014

Earlier work this paper cites.

Krizhevsky, A., Nair, V., Hinton, G.: The cifar-10 dataset. online: http://www. cs. toronto. edu/kriz/cifar. html (2014)

2014

Earlier work this paper cites.

Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

Original

2014

Earlier work this paper cites.

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: Proceedings of the 22nd ACM international conference on Multimedia. pp. 177–186. ACM (2014)

2014

Cited alongside, same era.

Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

Original

2015

Cited alongside, same era.

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396 (2016)

Original

2016

Cited alongside, same era.

Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)

Original

2016

Cited alongside, same era.

Lopez-Paz, D., et al.: Gradient episodic memory for continual learning. In: Advances in Neural Information Processing Systems. pp. 6470–6479 (2017)

2017

Later among the works it cites.

Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 2642–2651. PMLR, International Convention Centre, Sydney, Australia (06–11 Aug 2017)

2017

Later among the works it cites.

Rebuffi, S.A., Kolesnikov, A., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: Proc. CVPR (2017)

2017

Later among the works it cites.

Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI. vol. 4, p. 12 (2017)

2017

Later among the works it cites.