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Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting.
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Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2004
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Alex Krizhevsky et al · 2009
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Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2010
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Wolfgang Maass · 2014
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Deep residual learning for image recognition. corr abs/1512.03385 (2015), 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Hadi Pouransari and Saman Ghili · 2015
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Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
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Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
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Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina · 2019
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A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2019
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Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
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Biologically inspired sleep algorithm for artificial neural networks
Giri P Krishnan, Timothy Tadros, Ramyaa Ramyaa, and Maxim Bazhenov · 2019
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German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Lifelong learning with dynamically expandable networks
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Measuring and regularizing networks in function space
Ari S Benjamin, David Rolnick, and Konrad Kording · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
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Complementary learning for overcoming catastrophic forgetting using experience replay
Mohammad Rostami, Soheil Kolouri, and Praveen K Pilly · 2019
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Three scenarios for continual learning
Gido M van de Ven and Andreas S Tolias · 2019
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Orthogonal gradient descent for continual learning
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Generalized class incremental learning
Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, and Boi Faltings · 2020
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Online class-incremental continual learning with adversarial shapley value
Dongsub Shim, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, and Jongseong Jang · 2020
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2020
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Noise as a resource for learning in knowledge distillation
Elahe Arani, Fahad Sarfraz, and Bahram Zonooz · 2021
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Replay in deep learning: Current approaches and missing biological elements
Tyler L Hayes, Giri P Krishnan, Maxim Bazhenov, Hava T Siegelmann, Terrence J Sejnowski, and Christopher Kanan · 2021
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Knowledge distillation beyond model compression
Fahad Sarfraz, Elahe Arani, and Bahram Zonooz · 2021
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Online continual learning in image classification: An empirical survey
Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner · 2022
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