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Deploying sophisticated deep learning models on embedded devices with the purpose of solving real-world problems is a struggle using today's technology.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Guanyu Zhou, Kihyuk Sohn, and Honglak Lee · 2012
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Christiane Lemke, Marcin Budka, and Bogdan Gabrys · 2015
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Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, and Scott Yang · 2017
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Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Sergey Ioffe · 2017
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David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Vincenzo Lomonaco, Davide Maltoni, and Lorenzo Pellegrini · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2019
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Example on-device model personalization with tensorflow lite, Dec 2019
Pavel Senchanka · 2019
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Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias · 2019
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Sebastian Farquhar and Yarin Gal · 2018
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Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
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Dawei Li, Serafettin Tasci, Shalini Ghosh, Jingwen Zhu, Junting Zhang, and Larry Heck · 2019
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Keval Doshi and Yasin Yilmaz · 2020
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Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
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