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Class-incremental learning (CIL) aims to recognize new classes incrementally while maintaining the discriminability of old classes.
Episodic and semantic memory
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Catastrophic interference in connectionist networks: The sequential learning problem
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The mnist database of handwritten digits
Yann LeCun · 1998
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The psychology and neuroscience of forgetting
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Learning multiple layers of features from tiny images
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Imagenet: A large-scale hierarchical image database
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Herding dynamical weights to learn
Max Welling · 2009
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Learning with marginalized corrupted features
Laurens Maaten, Minmin Chen, Stephen Tyree, and Kilian Weinberger · 2013
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob J. Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Tiny imagenet visual recognition challenge
Hadi Pouransari and Saman Ghili · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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What learning systems do intelligent agents need? complementary learning systems theory updated
Dharshan Kumaran, Demis Hassabis, and James L McClelland · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, et al · 2016
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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
J. Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, et al · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and S. Ganguli · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, A. 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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2018
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Memory replay gans: Learning to generate new categories without forgetting
Chenshen Wu, L. Herranz, X. Liu, Y. Wang, Joost van de Weijer, and B. Raducanu · 2018
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, et al · 2018
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, et al · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Y. Liu, and Z. Kira · 2018
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, et al · 2019
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and D. Lin · 2019
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Overcoming catastrophic forgetting with unlabeled data in the wild
Kibok Lee, Kimin Lee, Jinwoo Shin, and Honglak Lee · 2019
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Large scale incremental learning
Y. Wu, Yan-Jia Chen, Lijuan Wang, et al · 2019
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
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Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
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Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, et al · 2019
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Continual learning in neural networks
Rahaf Aljundi · 2019
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Three scenarios for continual learning
Gido M. van de Ven and A. Tolias · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu · 2020
Cited alongside, same era.
Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, et al · 2020
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Maintaining discrimination and fairness in class incremental learning
Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia · 2020
Cited alongside, same era.
Scail: Classifier weights scaling for class incremental learning
Eden Belouadah and Adrian Popescu · 2020
Cited alongside, same era.
Topology-preserving class-incremental learning
Xiaoyu Tao, Xinyuan Chang, Xiaopeng Hong, Xing Wei, and Yihong Gong · 2020
Three types of incremental learning
Gido M van de Ven, Tinne Tuytelaars, and Andreas S Tolias · 2022
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Mimicking the oracle: an initial phase decorrelation approach for class incremental learning
Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip HS Torr, Song Bai, and Vincent YF Tan · 2022
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Learning to imagine: Diversify memory for incremental learning using unlabeled data
Yu-Ming Tang, Yi-Xing Peng, and Wei-Shi Zheng · 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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Dataset knowledge transfer for class-incremental learning without memory
Habib Slim, Eden Belouadah, Adrian Popescu, and Darian Onchis · 2022
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Class-incremental continual learning into the extended der-verse
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Cited alongside, same era.
Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, X. Liu, L. Herranz, et al · 2020
Cited alongside, same era.
Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
Cited alongside, same era.
Self-supervised label augmentation via input transformations
Hankook Lee, Sung Ju Hwang, and Jinwoo Shin · 2020
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.
Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
Cited alongside, same era.
Lifelong machine learning with deep streaming linear discriminant analysis
Tyler L Hayes and Christopher Kanan · 2020
Cited alongside, same era.
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, and Simone Calderara · 2022
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Class-incremental learning with cross-space clustering and controlled transfer
Arjun Ashok, KJ Joseph, and Vineeth N Balasubramanian · 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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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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A multi-head model for continual learning via out-of-distribution replay
Gyuhak Kim, Bing Liu, and Zixuan Ke · 2022
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Wide neural networks forget less catastrophically
Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, and Mehrdad Farajtabar · 2022
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A theoretical study on solving continual learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, and Bing Liu · 2022
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Long-tailed class incremental learning
Xialei Liu, Yu-Song Hu, Xu-Sheng Cao, Andrew D Bagdanov, Ke Li, and Ming-Ming Cheng · 2022
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Self-supervised models are continual learners
Enrico Fini, Victor G Turrisi Da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, and Julien Mairal · 2022
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Dynamic support network for few-shot class incremental learning
Boyu Yang, Mingbao Lin, Yunxiao Zhang, Binghao Liu, Xiaodan Liang, Rongrong Ji, and Qixiang Ye · 2022
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Ensemble deep learning: A review
Mudasir A Ganaie, Minghui Hu, AK Malik, M Tanveer, and PN Suganthan · 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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Beef: Bi-compatible class-incremental learning via energy-based expansion and fusion
Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, and Peilin Zhao · 2022
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Rethinking confidence calibration for failure prediction
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-Lin Liu · 2022
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Deep class-incremental learning: A survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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Bilateral memory consolidation for continual learning
Xing Nie, Shixiong Xu, Xiyan Liu, Gaofeng Meng, Chunlei Huo, and Shiming Xiang · 2023
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Optimizing mode connectivity for class incremental learning
Haitao Wen, Haoyang Cheng, Heqian Qiu, Lanxiao Wang, Lili Pan, and Hongliang Li · 2023
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Class incremental learning via likelihood ratio based task prediction
Haowei Lin, Yijia Shao, Weinan Qian, Ningxin Pan, Yiduo Guo, and Bing Liu · 2023
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Learnability and algorithm for continual learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, and Bing Liu · 2023
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Imitating the oracle: Towards calibrated model for class incremental learning
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-Lin Liu · 2023
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Ddgr: Continual learning with deep diffusion-based generative replay
Rui Gao and Weiwei Liu · 2023
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Dkt: Diverse knowledge transfer transformer for class incremental learning
Xinyuan Gao, Yuhang He, Songlin Dong, Jie Cheng, Xing Wei, and Yihong Gong · 2023
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Dense network expansion for class incremental learning
Zhiyuan Hu, Yunsheng Li, Jiancheng Lyu, Dashan Gao, and Nuno Vasconcelos · 2023
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Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogerio Feris, and Zsolt Kira · 2023
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Pivot: Prompting for video continual learning
Andrés Villa, Juan León Alcázar, Motasem Alfarra, Kumail Alhamoud, Julio Hurtado, Fabian Caba Heilbron, Alvaro Soto, and Bernard Ghanem · 2023
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Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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Class incremental learning with pre-trained vision-language models
Xialei Liu, Xusheng Cao, Haori Lu, Jia-wen Xiao, Andrew D Bagdanov, and Ming-Ming Cheng · 2023
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Continual evaluation for lifelong learning: Identifying the stability gap
Matthias De Lange, Gido van de Ven, and Tinne Tuytelaars · 2023
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Class incremental learning: A review and performance evaluation
Fei Zhu, Xu-Yao Zhang, and Cheng-Lin Liu · 2023
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Average of pruning: Improving performance and stability of out-of-distribution detection
Zhen Cheng, Fei Zhu, Xu-Yao Zhang, and Cheng-Lin Liu · 2023
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Learnable distribution calibration for few-shot class-incremental learning
Binghao Liu, Boyu Yang, Lingxi Xie, Ren Wang, Qi Tian, and Qixiang Ye · 2023
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On the stability-plasticity dilemma of class-incremental learning
Dongwan Kim and Bohyung Han · 2023
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A comprehensive survey of continual learning: theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2024
Closest in time.
Generative multi-modal models are good class incremental learners
Xusheng Cao, Haori Lu, Linlan Huang, Xialei Liu, and Ming-Ming Cheng · 2024
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Continual forgetting for pre-trained vision models
Hongbo Zhao, Bolin Ni, Junsong Fan, Yuxi Wang, Yuntao Chen, Gaofeng Meng, and Zhaoxiang Zhang · 2024
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Branch-tuning: Balancing stability and plasticity for continual self-supervised learning
Wenzhuo Liu, Fei Zhu, and Cheng-Lin Liu · 2024
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Federated class-incremental learning with prototype guided transformer
Haiyang Guo, Fei Zhu, Wenzhuo Liu, Xu-Yao Zhang, and Cheng-Lin Liu · 2024
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Esdb: Expand the shrinking decision boundary via one-to-many information matching for continual learning with small memory
Kunchi Li, Hongyang Chen, Jun Wan, and Shan Yu · 2024
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