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The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems.
Gradient based sample selection for online continual learning
Aljundi, R., Lin, M., Goujaud, B., and Bengio, Y · 1903
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Online continual learning with maximally interfered retrieval
Aljundi, R., Caccia, L., Belilovsky, E., Caccia, M., Lin, M., Charlin, L., and Tuytelaars, T · 1908
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Simplified neuron model as a principal component analyzer
Oja, E · 1982
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Heterosynaptic plasticity underlies aversive olfactory learning in ¡em¿drosophila¡/em¿
Hige, T., Aso, Y., Modi, M. N., Rubin, G. M., and Turner, G. C · 2015
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
Earlier work this paper cites.
Learning without forgetting
Li, Z. and Hoiem, D · 2017
Earlier work this paper cites.
Optimal degrees of synaptic connectivity
Litwin-Kumar, A., Harris, K. D., Axel, R., Sompolinsky, H., and Abbott, L. F · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M · 2017
Earlier work this paper cites.
Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Cited alongside, same era.
Deephyper: Asynchronous hyperparameter search for deep neural networks
Balaprakash, P., Salim, M., Uram, T., Vishwanath, V., and Wild, S · 2018
Cited alongside, same era.
Meta-learning representations for continual learning
Javed, K. and White, M · 2019
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Boah: A tool suite for multi-fidelity bayesian optimization & \& analysis of hyperparameters
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Marben, J., Müller, P., and Hutter, F · 2019
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Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2019
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Random path selection for continual learning
Rajasegaran, J., Hayat, M., Khan, S. H., Khan, F. S., and Shao, L · 2019
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Memristor design rules for dynamic learning and edge processing applications
Yanguas-Gil, A · 2019
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The insect brain as a model system for low power electronics and edge processing applications
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Chaudhry, A., Ranzato, M., Rohrbach, M., and Elhoseiny, M · 2018
Cited alongside, same era.
Towards robust evaluations of continual learning
Farquhar, S. and Gal, Y · 2018
Cited alongside, same era.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Hsu, Y.-C., Liu, Y.-C., Ramasamy, A., and Kira, Z · 2018
Cited alongside, same era.
Differentiable plasticity: training plastic neural networks with backpropagation
Miconi, T., Clune, J., and Stanley, K. O · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Schwarz, J., Czarnecki, W., Luketina, J., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R · 2018
Cited alongside, same era.
Generative replay with feedback connections as a general strategy for continual learning
van de Ven, G. M. and Tolias, A. S · 2018
Cited alongside, same era.
Class-incremental learning via deep model consolidation
Zhang, J., Zhang, J., Ghosh, S., Li, D., Tasci, S., Heck, L., Zhang, H., and Kuo, C.-C. J · 2018
Cited alongside, same era.
Yanguas-Gil, A., Mane, A., Elam, J. W., Wang, F., Severa, W., Daram, A. R., and Kudithipudi, D · 2019
Later among the works it cites.
Beaulieu, S., Frati, L., Miconi, T., Lehman, J., Stanley, K. O., Clune, J., and Cheney, N · 2020
Closest in time.
Dark experience for general continual learning: a strong, simple baseline
Buzzega, P., Boschini, M., Porrello, A., Abati, D., and Calderara, S · 2020
Closest in time.
A neural dirichlet process mixture model for task-free continual learning
Lee, S., Ha, J., Zhang, D., and Kim, G · 2020
Closest in time.
Pogodin, R. and Latham, P. E · 2020
Closest in time.
itaml: An incremental task-agnostic meta-learning approach
Rajasegaran, J., Khan, S., Hayat, M., Khan, F. S., and Shah, M · 2020
Closest in time.
Online continual learning in image classification: An empirical survey
Mai, Z., Li, R., Jeong, J., Quispe, D., Kim, H., and Sanner, S · 2021
Closest in time.