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Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners.
Random sampling with a reservoir
Jeffrey S Vitter · 1985
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Robustness and generalization
Huan Xu and Shie Mannor · 2012
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Stable and efficient spectral divide and conquer algorithms for the symmetric eigenvalue decomposition and the svd
Yuji Nakatsukasa and Nicholas J Higham · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Tiny ImageNet Challenge (CS231n), 2015
Stanford · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, Samy Bengio, et al · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 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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Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester, and Nati Srebro · 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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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 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
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 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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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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Towards Robust Evaluations of Continual Learning
Sebastian Farquhar and Yarin Gal · 2018
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Born again neural networks
Tommaso Furlanello, Zachary C Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 2018
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Three factors influencing minima in sgd
Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 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.
Overcoming Catastrophic Forgetting with Hard Attention to the Task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
Cited alongside, same era.
Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2018
Cited alongside, same era.
Three continual learning scenarios
Gido M van de Ven and Andreas S Tolias · 2018
Cited alongside, same era.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Dong Yin, Mehrdad Farajtabar, Ang Li, Nir Levine, and Alex Mott · 2020
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Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer · 2020
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Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
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Rainbow memory: Continual learning with a memory of diverse samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha, and Jonghyun Choi · 2021
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Co2l: Contrastive continual learning
Hyuntak Cha, Jaeho Lee, and Jinwoo Shin · 2021
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Aladin Virmaux and Kevin Scaman · 2018
Cited alongside, same era.
Towards fast computation of certified robustness for relu networks
Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, and Inderjit Dhillon · 2018
Cited alongside, same era.
Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney · 2018
Cited alongside, same era.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Cited alongside, same era.
Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, and Lucas Page-Caccia · 2019
Cited alongside, same era.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Cited alongside, same era.
Sorting out lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2019
Cited alongside, same era.
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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Remembering for the right reasons: Explanations reduce catastrophic forgetting
Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E Gonzalez, Marcus Rohrbach, and Trevor Darrell · 2021
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Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
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Training certifiably robust neural networks with efficient local lipschitz bounds
Yujia Huang, Huan Zhang, Yuanyuan Shi, J Zico Kolter, and Anima Anandkumar · 2021
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The lipschitz constant of self-attention
Hyunjik Kim, George Papamakarios, and Andriy Mnih · 2021
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Globally-robust neural networks
Klas Leino, Zifan Wang, and Matt Fredrikson · 2021
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Representational continuity for unsupervised continual learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, and Sung Ju Hwang · 2021
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An empirical investigation of the role of pre-training in lifelong learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell · 2021
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Regular polytope networks
Federico Pernici, Matteo Bruni, Claudio Baecchi, and Alberto Del Bimbo · 2021
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Dualnet: Continual learning, fast and slow
Quang Pham, Chenghao Liu, and Steven Hoi · 2021
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Lipschitz continuity guided knowledge distillation
Yuzhang Shang, Bin Duan, Ziliang Zong, Liqiang Nie, and Yan Yan · 2021
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Relating adversarially robust generalization to flat minima
David Stutz, Matthias Hein, and Bernt Schiele · 2021
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Rehearsal revealed: The limits and merits of revisiting samples in continual learning
Eli Verwimp, Matthias De Lange, and Tinne Tuytelaars · 2021
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Using hindsight to anchor past knowledge in continual learning
Arslan Chaudhry, Albert Gordo, Puneet K Dokania, Philip Torr, and David Lopez-Paz · 2021
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Gradient-based editing of memory examples for online task-free continual learning
Xisen Jin, Arka Sadhu, Junyi Du, and Xiang Ren · 2021
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Learning fast, learning slow: A general continual learning method based on complementary learning system
Elahe Arani, Fahad Sarfraz, and Bahram Zonooz · 2022
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Effects of auxiliary knowledge on continual learning
Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Lorenzo Bonicelli, Matteo Boschini, Simone Calderara, and Concetto Spampinato · 2022
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Class-incremental continual learning into the extended der-verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, and Simone Calderara · 2022
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Transfer without forgetting
Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello, Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Concetto Spampinato, and Simone Calderara · 2022
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Continual semi-supervised learning through contrastive interpolation consistency
Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, and Simone Calderara · 2022
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New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2022
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Self-supervised models are continual learners
Enrico Fini, Victor G Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, and Julien Mairal · 2022
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On measuring excess capacity in neural networks
Florian Graf, Sebastian Zeng, Marc Niethammer, and Roland Kwitt · 2022
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Learning smooth neural functions via lipschitz regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, and Or Litany · 2022
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Constructing stronger and faster baselines for skeleton-based action recognition
Yi-Fan Song, Zhang Zhang, Caifeng Shan, and Liang Wang · 2022
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Online coreset selection for rehearsal-based continual learning
Jaehong Yoon, Divyam Madaan, Eunho Yang, and Sung Ju Hwang · 2022
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Class-incremental continual learning into the extended der-verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, and Simone Calderara · 2022
Closest in time.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2022
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