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Efforts to overcome catastrophic forgetting have primarily centered around developing more effective Continual Learning (CL) methods.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world
Stephen Grossberg · 2013
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Continual learning with extended kronecker-factored approximate curvature
Janghyeon Lee, Hyeong Gwon Hong, Donggyu Joo, and Junmo Kim · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 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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Speciality vs Generality: An Empirical Study on Catastrophic Forgetting in Fine-tuning Foundation Models
Yong Lin, Lu Tan, Hangyu Lin, Zeming Zheng, Renjie Pi, Jipeng Zhang, Shizhe Diao, Haoxiang Wang, Han Zhao, Yuan Yao, and Tong Zhang · 2021
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Bns: Building network structures dynamically for continual learning
Qi Qin, Wenpeng Hu, Han Peng, Dongyan Zhao, and Bing Liu · 2021
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Learning with selective forgetting
Takashi Shibata, Go Irie, Daiki Ikami, and Yu Mitsuzumi · 2021
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Efficientnetv2: Smaller models and faster training
Mingxing Tan and Quoc Le · 2021
Gcr: Gradient coreset based replay buffer selection for continual learning
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, and Pradeep Shenoy · 2022
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On redundancy and diversity in cell-based neural architecture search
Xingchen Wan, Binxin Ru, Pedro M Esperança, and Zhenguo Li · 2022
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Foster: Feature boosting and compression for class-incremental learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2022
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A model or 603 exemplars: Towards memory-efficient class-incremental learning
Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, and De-Chuan Zhan · 2022
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Dynamic residual classifier for class incremental learning
Xiuwei Chen and Xiaobin Chang · 2023
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Cited alongside, same era.
Rethinking architecture selection in differentiable nas
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
Cited alongside, same era.
Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
Cited alongside, same era.
Overcoming catastrophic forgetting in incremental object detection via elastic response distillation
Tao Feng, Mang Wang, and Hangjie Yuan · 2022
Cited alongside, same era.
Forget-free continual learning with winning subnetworks
Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, and Chang D Yoo · 2022
Cited alongside, same era.
Wide neural networks forget less catastrophically
Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, and Mehrdad Farajtabar · 2022
Cited alongside, same era.
Architecture matters in continual learning
Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Gorur, and Mehrdad Farajtabar · 2022
Cited alongside, same era.
Tatsuya Konishi, Mori Kurokawa, Chihiro Ono, Zixuan Ke, Gyuhak Kim, and Bing Liu · 2023
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Pa&da: Jointly sampling path and data for consistent nas
Shun Lu, Yu Hu, Longxing Yang, Zihao Sun, Jilin Mei, Jianchao Tan, and Chengru Song · 2023
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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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Rlipv2: Fast scaling of relational language-image pre-training
Hangjie Yuan, Shiwei Zhang, Xiang Wang, Samuel Albanie, Yining Pan, Tao Feng, Jianwen Jiang, Dong Ni, Yingya Zhang, and Deli Zhao · 2023
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Does continual learning equally forget all parameters?
Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang · 2023
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Pycil: A python toolbox for class-incremental learning, 2023
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, and De-Chuan Zhan · 2023
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WinGNN: dynamic graph neural networks with random gradient aggregation window
Yifan Zhu, Fangpeng Cong, Dan Zhang, Wenwen Gong, Qika Lin, Wenzheng Feng, Yuxiao Dong, and Jie Tang · 2023
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Make continual learning stronger via c-flat, 2024
Ang Bian, Wei Li, Hangjie Yuan, Chengrong Yu, Zixiang Zhao, Mang Wang, Aojun Lu, and Tao Feng · 2024
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