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Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data.
In: Psychology of learning and motivation, vol. 24, pp. 109–165. Elsevier (1989)
McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem · 1989
Earlier work this paper cites.
Neural computation 10
Amari, S.I.: Natural gradient works efficiently in learning · 1998
Earlier work this paper cites.
Trends in Cognitive Sciences 3
French, R.M.: Catastrophic forgetting in connectionist networks · 1999
Earlier work this paper cites.
In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. Ieee (2009)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database · 2009
Earlier work this paper cites.
Technical report (2009)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images · 2009
Earlier work this paper cites.
In: Neural Networks: Tricks of the Trade: Second Edition, pp. 479–535. Springer (2012)
Martens, J., Sutskever, I.: Training deep and recurrent networks with hessian-free optimization · 2012
Earlier work this paper cites.
Frontiers in Psychology 4
Mermillod, M., Bugaiska, A., BONIN, P.: The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects · 2013
Earlier work this paper cites.
arXiv preprint arXiv:1412.6980 (2014)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization · 2014
Earlier work this paper cites.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 770–778 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition · 2015
Earlier work this paper cites.
arXiv preprint arXiv:1503.02531 (2015)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network · 2015
Earlier work this paper cites.
arXiv preprint arXiv:1607.00122 (2016)
Jung, H., Ju, J., Jung, M., Kim, J.: Less-forgetting learning in deep neural networks · 2016
Earlier work this paper cites.
Proceedings of the national academy of sciences 114
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks · 2017
Earlier work this paper cites.
IEEE transactions on pattern analysis and machine intelligence 40
Li, Z., Hoiem, D.: Learning without forgetting · 2017
Earlier work this paper cites.
In: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Lopez-Paz, D., Ranzato, M.A.: Gradient episodic memory for continual learning · 2017
Earlier work this paper cites.
In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2001–2010 (2017)
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning · 2017
Earlier work this paper cites.
In: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay · 2017
Earlier work this paper cites.
Technical report (2017)
Wu, J., Zhang, Q., Xu, G.: Tiny imagenet challenge · 2017
Earlier work this paper cites.
In: D. Precup, Y.W. Teh (eds.) Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research , vol. 70, pp. 3987–3995. PMLR (2017)
Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence · 2017
Earlier work this paper cites.
In: Proceedings of the European conference on computer vision (ECCV), pp. 139–154 (2018)
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget · 2018
Earlier work this paper cites.
In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
Belouadah, E., Popescu, A.: Deesil: Deep-shallow incremental learning · 2018
Earlier work this paper cites.
In: Proceedings of the European conference on computer vision (ECCV), pp. 233–248 (2018)
Castro, F.M., Marín-Jiménez, M.J., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning · 2018
Earlier work this paper cites.
In: Proceedings of the European conference on computer vision (ECCV), pp. 532–547 (2018)
Chaudhry, A., Dokania, P.K., Ajanthan, T., Torr, P.H.: Riemannian walk for incremental learning: Understanding forgetting and intransigence · 2018
Earlier work this paper cites.
Proceedings of the National Academy of Sciences 115
Huszár, F.: Note on the quadratic penalties in elastic weight consolidation · 2018
Earlier work this paper cites.
In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2262–2268. IEEE (2018)
Liu, X., Masana, M., Herranz, L., Van de Weijer, J., Lopez, A.M., Bagdanov, A.D.: Rotate your networks: Better weight consolidation and less catastrophic forgetting · 2018
Earlier work this paper cites.
Advances in Neural Information Processing Systems 31
Ritter, H., Botev, A., Barber, D.: Online structured laplace approximations for overcoming catastrophic forgetting · 2018
Cited alongside, same era.
In: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)
Aljundi, R., Belilovsky, E., Tuytelaars, T., Charlin, L., Caccia, M., Lin, M., Page-Caccia, L.: Online continual learning with maximal interfered retrieval · 2019
Cited alongside, same era.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Aljundi, R., Kelchtermans, K., Tuytelaars, T.: Task-free continual learning · 2019
Cited alongside, same era.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Belouadah, E., Popescu, A.: Il2m: Class incremental learning with dual memory · 2019
Cited alongside, same era.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 831–839 (2019)
In: European Conference on Computer Vision, pp. 423–439. Springer (2022)
Gao, Q., Zhao, C., Ghanem, B., Zhang, J.: R-dfcil: Relation-guided representation learning for data-free class incremental learning · 2022
Later among the works it cites.
SIAM Journal on Imaging Sciences 15
Grementieri, L., Fioresi, R.: Model-centric data manifold: the data through the eyes of the model · 2022
Later among the works it cites.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16071–16080 (2022)
Kang, M., Park, J., Han, B.: Class-incremental learning by knowledge distillation with adaptive feature consolidation · 2022
Later among the works it cites.
IEEE Transactions on Geoscience and Remote Sensing 60
Liu, W., Nie, X., Zhang, B., Sun, X.: Incremental learning with open-set recognition for remote sensing image scene classification · 2022
Later among the works it cites.
IEEE Transactions on Pattern Analysis and Machine Intelligence 45
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 · 2022
Later among the works it cites.
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Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing · 2019
Cited alongside, same era.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning · 2019
Cited alongside, same era.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Xiang, Y., Fu, Y., Ji, P., Huang, H.: Incremental learning using conditional adversarial networks · 2019
Cited alongside, same era.
Advances in neural information processing systems 33
Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline · 2020
Cited alongside, same era.
In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX 16, pp. 86–102. Springer (2020)
Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: Podnet: Pooled outputs distillation for small-tasks incremental learning · 2020
Cited alongside, same era.
The Lancet Digital Health 2
Lee, C.S., Lee, A.Y.: Clinical applications of continual learning machine learning · 2020
Cited alongside, same era.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 226–227 (2020)
Liu, X., Wu, C., Menta, M., Herranz, L., Raducanu, B., Bagdanov, A.D., Jui, S., de Weijer, J.v.: Generative feature replay for class-incremental learning · 2020
Cited alongside, same era.
The Journal of Machine Learning Research 21
Martens, J.: New insights and perspectives on the natural gradient method · 2020
Cited alongside, same era.
Journal of Intelligent & Robotic Systems 105
Shaheen, K., Hanif, M.A., Hasan, O., Shafique, M.: Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks · 2022
Later among the works it cites.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16732–16741 (2022)
Toldo, M., Ozay, M.: Bring evanescent representations to life in lifelong class incremental learning · 2022
Later among the works it cites.
Nature Machine Intelligence 4
van de Ven, G.M., Tuytelaars, T., Tolias, A.S.: Three types of incremental learning · 2022
Later among the works it cites.
In: Advances in Neural Information Processing Systems, vol. 35, pp. 14771–14783 (2022)
Zhang, Y., Pfahringer, B., Frank, E., Bifet, A., Lim, N.J.S., Jia, Y.: A simple but strong baseline for online continual learning: Repeated augmented rehearsal · 2022
Later among the works it cites.
In: The Eleventh International Conference on Learning Representations (2022)
Zhou, D.W., Wang, Q.W., Ye, H.J., Zhan, D.C.: A model or 603 exemplars: Towards memory-efficient class-incremental learning · 2022
Later among the works it cites.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9296–9305 (2022)
Zhu, K., Zhai, W., Cao, Y., Luo, J., Zha, Z.J.: Self-sustaining representation expansion for non-exemplar class-incremental learning · 2022
Later among the works it cites.
Artificial Intelligence Review 56
Ahmed, S.F., Alam, M.S.B., Hassan, M., Rozbu, M.R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A.B.M., Gandomi, A.H.: Deep learning modelling techniques: current progress, applications, advantages, and challenges · 2023
Later among the works it cites.
In: Thirty-seventh Conference on Neural Information Processing Systems (2023)
Goswami, D., Liu, Y., Twardowski, B., van de Weijer, J.: FeCAM: Exploiting the heterogeneity of class distributions in exemplar-free continual learning · 2023
Later among the works it cites.
Transactions on Machine Learning Research (2023)
Gowda, S., Zonooz, B., Arani, E.: Dual cognitive architecture: Incorporating biases and multi-memory systems for lifelong learning · 2023
Later among the works it cites.
In: The Eleventh International Conference on Learning Representations (2023)
Lange, M.D., van de Ven, G.M., Tuytelaars, T.: Continual evaluation for lifelong learning: Identifying the stability gap · 2023
Later among the works it cites.
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3911–3920 (2023)
Petit, G., Popescu, A., Schindler, H., Picard, D., Delezoide, B.: Fetril: Feature translation for exemplar-free class-incremental learning · 2023
Later among the works it cites.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3518–3528 (2023)
Soutif-Cormerais, A., Carta, A., Cossu, A., Hurtado, J., Lomonaco, V., Van de Weijer, J., Hemati, H.: A comprehensive empirical evaluation on online continual learning · 2023
Later among the works it cites.
arXiv preprint arXiv:2302.03648 (2023)
Zhou, D.W., Wang, Q.W., Qi, Z.H., Ye, H.J., Zhan, D.C., Liu, Z.: Deep class-incremental learning: A survey · 2023
Later among the works it cites.
Pattern Recognition Letters 180
Magistri, S., Baracchi, D., Shullani, D., Bagdanov, A.D., Piva, A.: Continual learning for adaptive social network identification · 2024
Later among the works it cites.
In: The Twelfth International Conference on Learning Representations (2024)
Magistri, S., Trinci, T., Soutif, A., van de Weijer, J., Bagdanov, A.D.: Elastic feature consolidation for cold start exemplar-free incremental learning · 2024
Later among the works it cites.
Transactions on Machine Learning Research (2024)
Verwimp, E., Aljundi, R., Ben-David, S., Bethge, M., Cossu, A., Gepperth, A., Hayes, T.L., Hüllermeier, E., Kanan, C., Kudithipudi, D., Lampert, C.H., Mundt, M., Pascanu, R., Popescu, A., Tolias, A.S., van de Weijer, J., Liu, B., Lomonaco, V., Tuytelaars, T., van de Ven, G.M.: Continual learning: Applications and the road forward · 2024
Later among the works it cites.
Neural Networks 184
Wang, C., Jiang, J., Hu, X., Liu, X., Ji, X.: Enhancing consistency and mitigating bias: A data replay approach for incremental learning · 2024
Later among the works it cites.
IEEE Transactions on Pattern Analysis and Machine Intelligence pp. 1–20 (2024)
Wang, L., Zhang, X., Su, H., Zhu, J.: A comprehensive survey of continual learning: Theory, method and application · 2024
Later among the works it cites.