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Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data.
Catastrophic forgetting, rehearsal and pseudorehearsal
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Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Attention is all you need
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
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Language models are few-shot learners
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An image is worth 16x16 words: Transformers for image recognition at scale
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On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
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Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2019
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Memory efficient experience replay for streaming learning
Tyler L Hayes, Nathan D Cahill, and Christopher Kanan · 2019
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Lifelong machine learning with deep streaming linear discriminant analysis
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Quantifying the carbon emissions of machine learning
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A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
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Rehearsal-free continual learning over small non-iid batches
Vincenzo Lomonaco, Davide Maltoni, and Lorenzo Pellegrini · 2020
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Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
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Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha, and Jonghyun Choi · 2021
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Dual-teacher class-incremental learning with data-free generative replay
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Always be dreaming: A new approach for data-free class-incremental learning
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Improving vision transformers for incremental learning
Pei Yu, Yinpeng Chen, Ying Jin, and Zicheng Liu · 2021
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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
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R-dfcil: Relation-guided representation learning for data-free class incremental learning
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Online continual learning in image classification: An empirical survey
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A closer look at rehearsal-free continual learning
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S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
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Learning to prompt for continual learning
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