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Although data-free incremental learning methods are memory-friendly, accurately estimating and counteracting representation shifts is challenging in the absence of historical data.
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Expert gate: Lifelong learning with a network of experts
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars · 2017
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Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
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Core50: a new dataset and benchmark for continuous object recognition
Vincenzo Lomonaco and Davide Maltoni · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2017
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Encoder based lifelong learning
Amal Rannen, Rahaf Aljundi, Matthew B Blaschko, and Tinne Tuytelaars · 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 with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, and Rama Chellappa · 2018
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Exemplar-supported generative reproduction for class incremental learning
Chen He, Ruiping Wang, Shiguang Shan, and Xilin Chen · 2018
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Rotate your networks: Better weight consolidation and less catastrophic forgetting
Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
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Theoretical insights into memorization in gans
Vaishnavh Nagarajan, Colin Raffel, and Ian J Goodfellow · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 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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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Memory replay gans: Learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, and Bogdan Raducanu · 2018
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Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
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Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
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Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
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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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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Overcoming catastrophic forgetting for continual learning via model adaptation
Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao Tao, Dongyan Zhao, Jinwen Ma, and Rui Yan · 2019
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
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Few-shot class-incremental learning via relation knowledge distillation
Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Adaptive aggregation networks for class-incremental learning
Yaoyao Liu, Bernt Schiele, and Qianru Sun · 2021
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An empirical investigation of the role of pre-training in lifelong learning
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Compacting, picking and growing for unforgetting continual learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 2019
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 2019
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Learning to remember: A synaptic plasticity driven framework for continual learning
Oleksiy Ostapenko, Mihai Puscas, Tassilo Klein, Patrick Jahnichen, and Moin Nabi · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Incremental learning using conditional adversarial networks
Ye Xiang, Ying Fu, Pan Ji, and Hua Huang · 2019
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Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, and Emma Strubell · 2021
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Dualnet: Continual learning, fast and slow
Quang Pham, Chenghao Liu, and Steven Hoi · 2021
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Continual learning via bit-level information preserving
Yujun Shi, Li Yuan, Yunpeng Chen, and Jiashi Feng · 2021
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Always be dreaming: A new approach for data-free class-incremental learning
James Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2021
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Acae-remind for online continual learning with compressed feature replay
Kai Wang, Joost van de Weijer, and Luis Herranz · 2021
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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Factual probing is [mask]: Learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen · 2021
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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 · 2021
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Exploring visual prompts for adapting large-scale models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola · 2022
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Adaptformer: Adapting vision transformers for scalable visual recognition
Shoufa Chen, GE Chongjian, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, and Ping Luo · 2022
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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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Visual prompt tuning for test-time domain adaptation
Yunhe Gao, Xingjian Shi, Yi Zhu, Hao Wang, Zhiqiang Tang, Xiong Zhou, Mu Li, and Dimitris N Metaxas · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Class-incremental learning by knowledge distillation with adaptive feature consolidation
Minsoo Kang, Jaeyoo Park, and Bohyung Han · 2022
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Few-shot class-incremental learning via entropy-regularized data-free replay
Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, and Jin Tang · 2022
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Online continual learning in image classification: An empirical survey
Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner · 2022
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Effect of scale on catastrophic forgetting in neural networks
Vinay Venkatesh Ramasesh, Aitor Lewkowycz, and Ethan Dyer · 2022
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Bring evanescent representations to life in lifelong class incremental learning
Marco Toldo and Mete Ozay · 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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S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
Yabin Wang, Zhiwu Huang, and Xiaopeng Hong · 2022
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Improving task-free continual learning by distributionally robust memory evolution
Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, and Mingchen Gao · 2022
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Dualprompt: Complementary prompting for rehearsal-free continual learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al · 2022
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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
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Class-incremental learning with strong pre-trained models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, and Stefano Soatto · 2022
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Prompt vision transformer for domain generalization
Zangwei Zheng, Xiangyu Yue, Kai Wang, and Yang You · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Self-sustaining representation expansion for non-exemplar class-incremental learning
Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, and Zheng-Jun Zha · 2022
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Fetril: Feature translation for exemplar-free class-incremental learning
Grégoire Petit, Adrian Popescu, Hugo Schindler, David Picard, and Bertrand Delezoide · 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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