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Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen · 1989
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Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
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An empirical investigation of catastrophic forgeting in gradient-based neural networks
Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, and Yoshua Bengio · 2013
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander I Kolesnikov, Georg Sperl, and Christoph H. Lampert · 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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Overcoming catastrophic interference by conceptors
Xu He and Herbert Jaeger · 2017
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Overcoming catastrophic forgetting in neural networks
James N Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, and et. al · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang · 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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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht · 2018
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, and Andrew Wilson · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Overcoming catastrophic interference using conceptor-aided backpropagation
Xu He and Herbert Jaeger · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, and Zsolt Kira · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L Hayes, and Christopher Kanan · 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.
Continual lifelong learning with neural networks: A review
German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Cited alongside, same era.
Closed-loop gan for continual learning
Amanda Rios and Laurent Itti · 2018
Cited alongside, same era.
Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
Cited alongside, same era.
Policy consolidation for continual reinforcement learning
Christos Kaplanis, Murray Shanahan, and Claudia Clopath · 2019
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Attention-based structural-plasticity
Soheil Kolouri, Nicholas Ketz, Xinyun Zou, Jeffrey Krichmar, and Praveen Pilly · 2019
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Explaining landscape connectivity of low-cost solutions for multilayer nets
Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, and Sanjeev Arora · 2019
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Continual learning: A comparative study on how to defy forgetting in classification tasks
Matthias Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ale Leonardis, Gregory G. Slabaugh, and Tinne Tuytelaars · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P Lillicrap, and Greg Wayne · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Cited alongside, same era.
Few-shot self reminder to overcome catastrophic forgetting
Junfeng Wen, Yanshuai Cao, and Ruitong Huang · 2018
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jungtae Lee, and Sung Ju Hwang · 2018
Cited alongside, same era.
Continuous learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2018
Cited alongside, same era.
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Michael Zhu and Suyog Gupta · 2018
Cited alongside, same era.
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 2019
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Toward understanding catastrophic forgetting in continual learning
Cuong V Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, and Stefano Soatto · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, and et. al · 2019
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Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
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Functional regularisation for continual learning using gaussian processes
Michalis K Titsias, Jonathan Schwarz, Alexander G de G Matthews, Razvan Pascanu, and Yee Whye Teh · 2019
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Spurious valleys in one-hidden-layer neural network optimization landscapes
Luca Venturi, Afonso S. Bandeira, and Joan Bruna · 2019
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Prototype reminding for continual learning
Mengmi Zhang, Tao Wang, Joo Hwee Lim, and Jiashi Feng · 2019
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Shawn Beaulieu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth O Stanley, Jeff Clune, and Nick Cheney · 2020
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Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
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Dropout as an implicit gating mechanism for continual learning
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, and Hassan Ghasemzadeh · 2020
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Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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What is being transferred in transfer learning?
Behnam Neyshabur, H. Sedghi, and Chiyuan Zhang · 2020
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Anatomy of catastrophic forgetting: Hidden representations and task semantics
Vinay V. Ramasesh, Ethan Dyer, and Maithra Raghu · 2020
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In the wild: From ml models to pragmatic ml systems
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, and Ali Farhadi · 2020
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Mitchell Wortsman, V. Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, J. Yosinski, and Ali Farhadi · 2020
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SOLA: Continual learning with second-order loss approximation
Dong Yin, Mehrdad Farajtabar, and Ang Li · 2020
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