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Continual learning needs to overcome catastrophic forgetting of the past.
A mathematical theory of communication
Claude Elwood Shannon · 1948
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The jpeg still picture compression standard
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
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Jpeg2000: Image compression fundamentals, standards and practice
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Hippocampal replay of extended experience
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Efficient volume sampling for row/column subset selection
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Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
Margaret F Carr, Shantanu P Jadhav, and Loren M Frank · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Determinantal point processes for machine learning
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Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory
James L McClelland · 2013
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Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Imagenet large scale visual recognition challenge
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Variable rate image compression with recurrent neural networks
George Toderici, Sean M O’Malley, Sung Jin Hwang, Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, and Rahul Sukthankar · 2015
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Soft-to-hard vector quantization for end-to-end learning compressible representations
Memory replay gans: Learning to generate new categories without forgetting
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Learning a unified classifier incrementally via rebalancing
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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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Online learned continual compression with adaptive quantization modules
Lucas Caccia, Eugene Belilovsky, Massimo Caccia, and Joelle Pineau · 2020
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Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, and Luc Van Gool · 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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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Full resolution image compression with recurrent neural networks
George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, and Michele Covell · 2017
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
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Continual learning in recurrent neural networks
Benjamin Ehret, Christian Henning, Maria R Cervera, Alexander Meulemans, Johannes Von Oswald, and Benjamin F Grewe · 2020
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Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
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High-fidelity generative image compression
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Soda10m: Towards large-scale object detection benchmark for autonomous driving
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Distilling causal effect of data in class-incremental learning
Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, and Hanwang Zhang · 2021
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Unsupervised class-incremental learning through confusion
Shivam Khare, Kun Cao, and James Rehg · 2021
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