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Continual learning poses a fundamental challenge for modern machine learning systems, requiring models to adapt to new tasks while retaining knowledge from previous ones.
Catastrophic forgetting in connectionist networks
Robert French · 1999
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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On the saddle point problem for non-convex optimization
Razvan Pascanu, Yann N. Dauphin, Surya Ganguli, and Yoshua Bengio · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
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Zhizhong Li and Derek Hoiem · 2016
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, 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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Singularity of the hessian in deep learning
Levent Sagun, Léon Bottou, and Yann LeCun · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Lifelong learning with dynamically expandable networks
Jeongtae Lee, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2017
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, and Tom Goldstein · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc' Aurelio Ranzato · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2017
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Tiny imagenet challenge
Jiayu Wu · 2017
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Lifelong Machine Learning
Zhiyuan Chen, Bing Liu, Ronald Brachman, Peter Stone, and Francesca Rossi · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
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Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Progress & compress: A scalable framework for continual learning, 2018
Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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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 · 2019
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Johanni Brea, Berfin Simsek, Bernd Illing, and Wulfram Gerstner · 2019
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Altersgd: Finding flat minima for continual learning by alternative training
Zhongzhan Huang, Mingfu Liang, Senwei Liang, and Wei He · 2021
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Class-incremental experience replay for continual learning under concept drift
Lukasz Korycki and Bartosz Krawczyk · 2021
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Avalanche: an end-to-end library for continual learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M. van de Ven, Martin Mundt, Qi She, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, and Davide Maltoni · 2021
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Center loss regularization for continual learning
Kaustubh Olpadkar and Ekta Gavas · 2021
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Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet Kumar Dokania, Philip H. S. Torr, and Marc’Aurelio Ranzato · 2019
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Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J. Zico Kolter · 2019
Cited alongside, same era.
Continual learning by asymmetric loss approximation with single-side overestimation
Dongmin Park, Seokil Hong, Bohyung Han, and Kyoung Mu Lee · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 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 Raymond Fu · 2019
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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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Linear mode connectivity in multitask and continual learning
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Dilan Görür, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
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Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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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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The role of permutation invariance in linear mode connectivity of neural networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, and Behnam Neyshabur · 2022
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Not just selection, but exploration: Online class-incremental continual learning via dual view consistency
Yanan Gu, Xu Yang, Kun Wei, and Cheng Deng · 2022
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Repair: Renormalizing permuted activations for interpolation repair, 2022
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, and Behnam Neyshabur · 2022
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Architecture matters in continual learning
Seyed-Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Görür, and Mehrdad Farajtabar · 2022
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Re-basin via implicit sinkhorn differentiation, 2022
Fidel A. Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail, Masih Aminbeidokhti, Eric Granger, and Marco Pedersoli · 2022
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Git re-basin: Merging models modulo permutation symmetries, 2023
Samuel K. Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
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Online bias correction for task-free continual learning
Aristotelis Chrysakis and Marie-Francine Moens · 2023
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Bert weaver: Using weight averaging to enable lifelong learning for transformer-based models in biomedical semantic search engines, 2023
Lisa Kühnel, Alexander Schulz, Barbara Hammer, and Juliane Fluck · 2023
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Mildly overparameterized relu networks have a favorable loss landscape, 2024
Kedar Karhadkar, Michael Murray, Hanna Tseran, and Guido Montúfar · 2024
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Weighted ensemble models are strong continual learners, 2024
Imad Eddine Marouf, Subhankar Roy, Enzo Tartaglione, and Stéphane Lathuilière · 2024
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Zipit! merging models from different tasks without training, 2024
George Stoica, Daniel Bolya, Jakob Bjorner, Pratik Ramesh, Taylor Hearn, and Judy Hoffman · 2024
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