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In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J · 1989
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Neural network classifiers estimate bayesian a posteriori probabilities
Richard, M. D. and Lippmann, R. P · 1991
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Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, A · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Catastrophic forgetting in connectionist networks
French, R. M · 1999
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Pascale 2 chal-lenge: Learning when test and training inputs have different distribu-tions challenge
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Dataset shift in machine learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2009
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., and Satheesh, S · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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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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Gradient episodic memory for continual learning
Lopez-Paz, D. and Ranzato, M · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., and Mazurowski, M. A · 2018
Cited alongside, same era.
Don’t forget, there is more than forgetting: new metrics for Continual Learning
Díaz-Rodríguez, N., Lomonaco, V., Filliat, D., and Maltoni, D · 2018
Cited alongside, same era.
Don’t forget, there is more than forgetting: new metrics for continual learning
Díaz-Rodríguez, N., Lomonaco, V., Filliat, D., and Maltoni, D · 2018
Cited alongside, same era.
New metrics and experimental paradigms for continual learning
Hayes, T. L., Kemker, R., Cahill, N. D., and Kanan, C · 2018
Cited alongside, same era.
Are we done with object recognition? The iCub robot’s perspective
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L · 2018
Cited alongside, same era.
Continuous learning in single-incremental-task scenarios
Maltoni, D. and Lomonaco, V · 2019
Later among the works it cites.
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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Iros 2019 lifelong robotic vision: Object recognition challenge [competitions]
Bae, H., Brophy, E., Chan, R. H., Chen, B., Feng, F., Graffieti, G., Goel, V., Hao, X., Han, H., Kanagarajah, S., et al · 2020
Closest in time.
Online fast adaptation and knowledge accumulation: a new approach to continual learning
Caccia, M., Rodriguez, P., Ostapenko, O., Normandin, F., Lin, M., Caccia, L., Laradji, I., Rish, I., Lacoste, A., Vazquez, D., et al · 2020
Closest in time.
Bypassing gradients re-projection with episodic memories in online continual learning
Chen, Y., Diethe, T., and Flach, P · 2020
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Online learned continual compression with adaptive quantization modules
Caccia, L., Belilovsky, E., Caccia, M., and Pineau, J · 2019
Cited alongside, same era.
On tiny episodic memories in continual learning, 2019
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., and Ranzato, M · 2019
Cited alongside, same era.
The animal-ai olympics
Crosby, M., Beyret, B., and Halina, M · 2019
Cited alongside, same era.
Differentially private continual learning
Farquhar, S. and Gal, Y · 2019
Cited alongside, same era.
Memory efficient experience replay for streaming learning
Hayes, T. L., Cahill, N. D., and Kanan, C · 2019
Cited alongside, same era.
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., and Díaz-Rodríguez, N · 2019
Cited alongside, same era.
Continual Learning with Deep Architectures
Lomonaco, V · 2019
Cited alongside, same era.
Automl@ neurips 2018 challenge: Design and results
Escalante, H. J., Tu, W.-W., Guyon, I., Silver, D. L., Viegas, E., Chen, Y., Dai, W., and Yang, Q · 2020
Closest in time.
Batch-level experience replay with review for continual learning, 2020
Mai, Z., Kim, H., Jeong, J., and Sanner, S · 2020
Closest in time.
Understanding the role of training regimes in continual learning
Mirzadeh, S. I., Farajtabar, M., Pascanu, R., and Ghasemzadeh, H · 2020
Closest in time.
Latent Replay for Real-Time Continual Learning
Pellegrini, L., Graffieti, G., Lomonaco, V., and Maltoni, D · 2020
Closest in time.
OpenLORIS-Object: A robotic vision dataset and benchmark for lifelong deep learning
She, Q., Feng, F., Hao, X., Yang, Q., Lan, C., Lomonaco, V., Shi, X., Wang, Z., Guo, Y., Zhang, Y., Qiao, F., and Chan, R. H. M · 2020
Closest in time.
Billion-scale semi-supervised learning for image classification
Yalniz, I. Z., Jégou, H., Chen, K., Paluri, M., and Mahajan, D · 2020
Closest in time.