Fetching the paper…
Reading the bibliography…
Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks without incurring forgetting.
M. McCloskey, N. J. Cohen, Catastrophic interference in connectionist networks: The sequential learning problem, Psychol Learn Motiv doi
1989
Earlier work this paper cites.
R. Ratcliff, Connectionist models of recognition memory: constraints imposed by learning and forgetting functions., Psychol. Rev. doi
1990
Earlier work this paper cites.
D. Yarowsky, Unsupervised word sense disambiguation rivaling supervised methods, in: ACL, 1995, doi
1995
Earlier work this paper cites.
C. Olivier, S. Bernhard, Z. Alexander, Semi-supervised learning, 2006, doi
2006
Earlier work this paper cites.
A. Krizhevsky, et al., Learning multiple layers of features from tiny images, Tech. rep. (2009)
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, A. Y. Ng, Reading digits in natural images with unsupervised feature learning, in: ANIPS, 2011
2011
Earlier work this paper cites.
D.-H. Lee, Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, in: ICML Workshop, 2013
2013
Earlier work this paper cites.
A. Tarvainen, H. Valpola, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, in: ANIPS, 2017
2017
Earlier work this paper cites.
D. Lopez-Paz, M. Ranzato, Gradient episodic memory for continual learning, in: ANIPS, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al., Overcoming catastrophic forgetting in neural networks, PNAS doi
2017
Earlier work this paper cites.
F. Zenke, B. Poole, S. Ganguli, Continual learning through synaptic intelligence, in: ICML, 2017
2017
Earlier work this paper cites.
Z. Li, D. Hoiem, Learning without forgetting, IEEE TPAMI doi
2017
Earlier work this paper cites.
S. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, icarl: Incremental classifier and representation learning, in: CVPR, 2017
2017
Cited alongside, same era.
S. Laine, T. Aila, Temporal ensembling for semi-supervised learning, in: ICLR, 2017
2017
Cited alongside, same era.
Z. Zheng, L. Zheng, Y. Yang, Unlabeled samples generated by gan improve the person re-identification baseline in vitro, in: ICCV, 2017
2017
Cited alongside, same era.
S. Farquhar, Y. Gal, Towards robust evaluations of continual learning, in: ICML Workshop, 2018
2018
Cited alongside, same era.
G. M. van de Ven, A. S. Tolias, Three continual learning scenarios, in: ANIPS Workshop, 2018
2018
Cited alongside, same era.
M. Riemer, I. Cases, R. Ajemian, M. Liu, I. Rish, Y. Tu, G. Tesauro, Learning to learn without forgetting by maximizing transfer and minimizing interference, in: ICLR, 2019
2019
Later among the works it cites.
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, C. A. Raffel, Mixmatch: A holistic approach to semi-supervised learning, in: ANIPS, 2019
2019
Later among the works it cites.
W. Zhou, S. Chang, N. Sosa, H. Hamann, D. Cox, Lifelong object detection, arXiv:2009.01129 (2020)
2020
Later among the works it cites.
J. Zhang, J. Zhang, S. Ghosh, D. Li, S. Tasci, L. Heck, H. Zhang, C.-C. J. Kuo, Class-incremental learning via deep model consolidation, in: WACV, 2020
2020
Later among the works it cites.
P. Buzzega, M. Boschini, A. Porrello, S. Calderara, Rethinking experience replay: a bag of tricks for continual learning, in: ICPR, 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
A. Mallya, S. Lazebnik, Packnet: Adding multiple tasks to a single network by iterative pruning, in: CVPR, 2018
2018
Cited alongside, same era.
J. Schwarz, W. Czarnecki, J. Luketina, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, R. Hadsell, Progress & compress: A scalable framework for continual learning, in: ICML, 2018
2018
Cited alongside, same era.
T. Miyato, S.-i. Maeda, M. Koyama, S. Ishii, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE TPAMI doi
2018
Cited alongside, same era.
A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, I. Goodfellow, Realistic evaluation of deep semi-supervised learning algorithms, in: ANIPS, 2018
2018
Cited alongside, same era.
H. Zhang, M. Cisse, Y. N. Dauphin, D. Lopez-Paz, mixup: Beyond empirical risk minimization, in: ICLR, 2018
2018
Cited alongside, same era.
R. Aljundi, K. Kelchtermans, T. Tuytelaars, Task-free continual learning, in: CVPR, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
P. Buzzega, M. Boschini, A. Porrello, D. Abati, S. Calderara, Dark experience for general continual learning: a strong, simple baseline, in: ANIPS, 2020
2020
Later among the works it cites.
A. Prabhu, P. H. Torr, P. K. Dokania, Gdumb: A simple approach that questions our progress in continual learning, in: ECCV, 2020
2020
Later among the works it cites.
Y. Pan, Multiple knowledge representation of artificial intelligence, Engineering doi
2020
Later among the works it cites.
D. Abati, J. Tomczak, T. Blankevoort, S. Calderara, R. Cucchiara, B. E. Bejnordi, Conditional channel gated networks for task-aware continual learning, in: CVPR, 2020
2020
Later among the works it cites.
M. De Lange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, T. Tuytelaars, A continual learning survey: Defying forgetting in classification tasks, IEEE TPAMI doi
2021
Closest in time.
A. Chaudhry, A. Gordo, P. K. Dokania, P. Torr, D. Lopez-Paz, Using hindsight to anchor past knowledge in continual learning, in: AAAI Conf. Artif. Intell., 2021
2021
Closest in time.
A. Lechat, S. Herbin, F. Jurie, Semi-supervised class incremental learning, in: ICPR, 2021
2021
Closest in time.
Y. Yang, Y. Zhuang, Y. Pan, Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies, Front. Inf. Technol. Electron. Eng. doi
2021
Closest in time.