FOSTER: Feature Boosting and Compression for Class-Incremental Learning

French, R.M.: Catastrophic forgetting in connectionist networks. Trends in cognitive sciences 3

1999

Earlier work this paper cites.

Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics 28

2000

Earlier work this paper cites.

Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Annals of statistics pp. 1189–1232 (2001)

2001

Earlier work this paper cites.

Golab, L., Özsu, M.T.: Issues in data stream management. ACM Sigmod Record 32

2003

Earlier work this paper cites.

Rubinstein, R.Y., Kroese, D.P.: The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning, vol. 133. Springer (2004)

2004

Earlier work this paper cites.

Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9

2008

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR. pp. 248–255. Ieee (2009)

2009

Earlier work this paper cites.

Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

2009

Earlier work this paper cites.

Zhou, Z.H.: Ensemble methods: foundations and algorithms. CRC press (2012)

2012

Earlier work this paper cites.

Grossberg, S.: Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural networks 37

2013

Earlier work this paper cites.

Hu, Z., Hong, L.J.: Kullback-leibler divergence constrained distributionally robust optimization. Available at Optimization Online pp. 1695–1724 (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. Advances in neural information processing systems 27

2014

Earlier work this paper cites.

Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2

Original

2015

Earlier work this paper cites.

Korattikara Balan, A., Rathod, V., Murphy, K.P., Welling, M.: Bayesian dark knowledge. Advances in Neural Information Processing Systems 28

2015

Earlier work this paper cites.

Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: KDD. pp. 785–794 (2016)

2016

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)

2016

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

Original

2016

Earlier work this paper cites.

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

Earlier work this paper cites.

Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., Pritzel, A., Wierstra, D.: Pathnet: Evolution channels gradient descent in super neural networks. arXiv preprint arXiv:1701.08734 (2017)

Original

2017

Cited alongside, same era.

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30

2017

Cited alongside, same era.

Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40

2017

Cited alongside, same era.

Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)

2017

Cited alongside, same era.

Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR. pp. 2001–2010 (2017)

Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., van de Weijer, J.: Class-incremental learning: survey and performance evaluation on image classification. arXiv preprint arXiv:2010.15277 (2020)

Original

2020

Later among the works it cites.

Wen, Y., Tran, D., Ba, J.: Batchensemble: an alternative approach to efficient ensemble and lifelong learning. arXiv preprint arXiv:2002.06715 (2020)

Original

2020

Later among the works it cites.

Zhao, B., Xiao, X., Gan, G., Zhang, B., Xia, S.T.: Maintaining discrimination and fairness in class incremental learning. In: CVPR. pp. 13208–13217 (2020)

2020

Later among the works it cites.

Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)

2021

Later among the works it cites.