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We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 1902
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Online continual learning with maximally interfered retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, and Tinne Tuytelaars · 1908
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Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
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The mnist database of handwritten digits, 1998
Yann LeCun, Corinna Cortes, and Christopher JC Burges · 1998
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey Hinton · 2009
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Structured prediction energy networks
David Belanger and Andrew McCallum · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 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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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 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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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
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Exemplar-supported generative reproduction for class incremental learning
Chen He, Ruiping Wang, Shiguang Shan, and Xilin Chen · 2018
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Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Nicolas Y Masse, Gregory D Grant, and David J Freedman · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Cited alongside, same era.
Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
Cited alongside, same era.
Task agnostic continual learning using online variational bayes
Chen Zeno, Itay Golan, Elad Hoffer, and Daniel Soudry · 2018
Cited alongside, same era.
Implicit generation and generalization in energy-based models
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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Gradient based memory editing for task-free continual learning
Xisen Jin, Arka Sadhu, Junyi Du, and Xiang Ren · 2020
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Sliced cramer synaptic consolidation for preserving deeply learned representations
Soheil Kolouri, Nicholas A Ketz, Andrea Soltoggio, and Praveen K Pilly · 2020
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A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
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Generative feature replay for class-incremental learning
Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D Bagdanov, Shangling Jui, and Joost van de Weijer · 2020
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Yilun Du and Igor Mordatch · 2019
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Cited alongside, same era.
Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Cited alongside, same era.
Overcoming catastrophic forgetting for continual learning via model adaptation
Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, and Rui Yan · 2019
Cited alongside, same era.
Generative models from the perspective of continual learning
Timothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz, Andrei Stoian, and David Filliat · 2019
Cited alongside, same era.
Continuous learning in single-incremental-task scenarios
Davide Maltoni and Vincenzo Lomonaco · 2019
Cited alongside, same era.
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu · 2019
Cited alongside, same era.
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
Cited alongside, same era.
Sandeep Madireddy, Angel Yanguas-Gil, and Prasanna Balaprakash · 2020
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Class-incremental learning: survey and performance evaluation
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer · 2020
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Martin Mundt, Yong Won Hong, Iuliia Pliushch, and Visvanathan Ramesh · 2020
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Continual deep learning by functional regularisation of memorable past
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E Turner, and Mohammad Emtiyaz Khan · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
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itaml: An incremental task-agnostic meta-learning approach
Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Mubarak Shah · 2020
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Functional regularisation for continual learning with gaussian processes
Michalis K Titsias, Jonathan Schwarz, Alexander G de G Matthews, Razvan Pascanu, and Yee Whye Teh · 2020
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Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
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Representation ensembling for synergistic lifelong learning with quasilinear complexity
Joshua T Vogelstein, Jayanta Dey, Hayden S Helm, Will LeVine, Ronak D Mehta, Ali Geisa, Haoyin Xu, Gido M van de Ven, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M White, and Carey E Priebe · 2020
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Supermasks in superposition
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
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Self-supervised learning aided class-incremental lifelong learning
Song Zhang, Gehui Shen, and Zhi-Hong Deng · 2020
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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, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Posterior meta-replay for continual learning
Christian Henning, Maria Cervera, Francesco D’Angelo, Johannes Von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F Grewe, and João Sacramento · 2021
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Natural continual learning: success is a journey, not (just) a destination
Ta-Chu Kao, Kristopher Jensen, Gido van de Ven, Alberto Bernacchia, and Guillaume Hennequin · 2021
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Continual learning in deep networks: an analysis of the last layer
Timothée Lesort, Thomas George, and Irina Rish · 2021
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Contextual transformation networks for online continual learning
Quang Pham, Chenghao Liu, Doyen Sahoo, and HOI Steven · 2021
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Class-incremental learning with generative classifiers
Gido M van de Ven, Zhe Li, and Andreas S Tolias · 2021
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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Continual evaluation for lifelong learning: Identifying the stability gap
Matthias De Lange, Gido van de Ven, and Tinne Tuytelaars · 2022
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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
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