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With the success of pretraining techniques in representation learning, a number of continual learning methods based on pretrained models have been proposed.
On Tiny Episodic Memories in Continual Learning, June 2019
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K. Dokania, Philip H. S. Torr, and Marc’Aurelio Ranzato · 1902
Earlier work this paper cites.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J. Cohen · 1989
Earlier work this paper cites.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J. Cohen · 1989
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2013
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Earlier work this paper cites.
Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 2017
Earlier work this paper cites.
iCaRL: Incremental Classifier and Representation Learning, April 2017
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
End-to-End Incremental Learning
Francisco M. Castro, Manuel J. Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
Cited alongside, same era.
Learning without Forgetting
Zhizhong Li and Derek Hoiem · 2018
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.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Large Scale Incremental Learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Cited alongside, same era.
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and SIMONE CALDERARA · 2020
Co2l: Contrastive continual learning
Hyuntak Cha, Jaeho Lee, and Jinwoo Shin · 2021
Later among the works it cites.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
ImageNet-21K Pretraining for the Masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
Later among the works it cites.
Scaling the Number of Tasks in Continual Learning, July 2022
Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez, Laurent Charlin, and Irina Rish · 2022
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Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
Cited alongside, same era.
GDumb: A Simple Approach that Questions Our Progress in Continual Learning
Ameya Prabhu, Philip H. S. Torr, and Puneet K. Dokania · 2020
Cited alongside, same era.
Self-Supervised Training Enhances Online Continual Learning
Jhair Gallardo
Cited in the paper.
Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, and Laurent Charlin · 2022
Closest in time.
How to train your vit? data, augmentation, and regularization in vision transformers
Andreas Peter Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2022
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DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning, August 2022
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
Closest in time.
Learning to Prompt for Continual Learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
Closest in time.