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Continual learning (CL) over non-stationary data streams remains one of the long-standing challenges in deep neural networks (DNNs) as they are prone to catastrophic forgetting.
Catastrophic forgetting in neural networks: the role of rehearsal mechanisms
A. Robins · 1993
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Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
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Multitask learning
Rich Caruana · 1997
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Tiny imagenet visual recognition challenge
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Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 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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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 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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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Large margin deep networks for classification
Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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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
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Unsupervised feature learning via non-parametric instance discrimination
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Multivariate confidence calibration for object detection
Fabian Küppers, Jan Kronenberger, Amirhossein Shantia, and Anselm Haselhoff · 2020
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Contrastive learning with hard negative samples
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2020
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When does self-supervision improve few-shot learning?
Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan · 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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Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M. Hospedales, and Huchuan Lu · 2018
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines, 2019
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Multi-task learning with deep neural networks: A survey, 2020
Michael Crawshaw · 2020
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Maintaining discrimination and fairness in class incremental learning
Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia · 2020
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Prototype augmentation and self-supervision for incremental learning
Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, and Cheng-Lin Liu · 2020
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Rethinking experience replay: a bag of tricks for continual learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2021
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Reducing representation drift in online continual learning
Lucas Caccia, Rahaf Aljundi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2021
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Are all negatives created equal in contrastive instance discrimination?, 2021
Tiffany Cai, Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Self-supervised training enhances online continual learning, 2021
Jhair Gallardo, Tyler L. Hayes, and Christopher Kanan · 2021
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Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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Few-shot lifelong learning
Pratik Mazumder, Pravendra Singh, and Piyush Rai · 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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