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Recently, continual learning (CL) has gained significant interest because it enables deep learning models to acquire new knowledge without forgetting previously learnt information.
“Gradient-based learning applied to document recognition,”
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, · 1998
Earlier work this paper cites.
“Measuring statistical dependence with Hilbert-Schmidt norms,”
A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, · 2005
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky and Geoffrey Hinton, · 2009
Earlier work this paper cites.
“iCaRL: Incremental classifier and representation learning,”
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert, · 2010
Earlier work this paper cites.
“Auto-encoding variational Bayes,”
D. P. Kingma and M. Welling, · 2013
Earlier work this paper cites.
“Tiny ImageNet visual recognition challenge,”
Ya Le and Xuan Yang, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2016
Earlier work this paper cites.
“Overcoming catastrophic forgetting in neural networks,”
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell, · 2017
Earlier work this paper cites.
“AdaNet: Adaptive structural learning of artificial neural networks,”
C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, and S. Yang, · 2017
Earlier work this paper cites.
“Less-forgetful learning for domain expansion in deep neural networks,”
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim, · 2018
Earlier work this paper cites.
“Kernel learning and optimization with Hilbert–Schmidt independence criterion,”
T. Wang and W. Li, · 2018
Earlier work this paper cites.
“Continual lifelong learning with neural networks: A review,”
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, · 2019
Cited alongside, same era.
“Efficient lifelong learning with A-GEM,”
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny, · 2019
Cited alongside, same era.
“Continual unsupervised representation learning,”
Dushyant Rao, Francesco Visin, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell, · 2019
Cited alongside, same era.
“Task-free continual learning,”
R. Aljundi, K. Kelchtermans, and T. Tuytelaars, · 2019
Cited alongside, same era.
“Continual unsupervised representation learning,”
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell, · 2019
Cited alongside, same era.
“Experience replay for continual learning,”
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, and Gregory Wayne, · 2019
“InfoVAEGAN: Learning joint interpretable representations by information maximization and maximum likelihood,”
Fei Ye and Adrian G. Bors, · 2021
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“Learning joint latent representations based on information maximization,”
Fei Ye and Adrian G Bors, · 2021
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“Lifelong twin generative adversarial networks,”
Fei Ye and Adrian G. Bors, · 2021
Later among the works it cites.
“Continual prototype evolution: Learning online from non-stationary data streams,”
Matthias De Lange and Tinne Tuytelaars, · 2021
Later among the works it cites.
“Lifelong teacher-student network learning,”
Fei Ye and Adrian Bors, · 2021
Later among the works it cites.
“Lifelong infinite mixture model based on knowledge-driven Dirichlet process,”
Fei Ye and Adrian G. Bors, · 2021
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Cited alongside, same era.
“Learning latent representations across multiple data domains using lifelong VAEGAN,”
Fei Ye and Adrian G. Bors, · 2020
Cited alongside, same era.
“Lifelong learning of interpretable image representations,”
Fei Ye and Adrian G. Bors, · 2020
Cited alongside, same era.
“A neural Dirichlet process mixture model for task-free continual learning,”
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim, · 2020
Cited alongside, same era.
“Mixtures of variational autoencoders,”
Fei Ye and Adrian G Bors, · 2020
Cited alongside, same era.
Later among the works it cites.
“Lifelong mixture of variational autoencoders,”
Fei Ye and Adrian G. Bors, · 2021
Later among the works it cites.
“Deep mixture generative autoencoders,”
Fei Ye and Adrian G. Bors, · 2021
Later among the works it cites.
“Gradient-based editing of memory examples for online task-free continual learning,”
Xisen Jin, Arka Sadhu, Junyi Du, and Xiang Ren, · 2021
Later among the works it cites.
“Lifelong generative modelling using dynamic expansion graph model,”
Fei Ye and Adrian G Bors, · 2022
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