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Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world.
Attention, similarity, and the identification–categorization relationship
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Ensembling neural networks: many could be better than all
Zhi-Hua Zhou, Jianxin Wu, and Wei Tang · 2002
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Interprocedural compatibility analysis for static object preallocation
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An efficient and backwards-compatible transformation to ensure memory safety of c programs
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and Fujie Huang · 2006
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Imagenet: A large-scale hierarchical image database
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Convolutional networks and applications in vision
Yann LeCun, Koray Kavukcuoglu, and Clément Farabet · 2010
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Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Ensemble methods: foundations and algorithms
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From n to n+ 1: Multiclass transfer incremental learning
Ilja Kuzborskij, Francesco Orabona, and Barbara Caputo · 2013
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Persistence of hippocampal multivoxel patterns into postencoding rest is related to memory
Arielle Tambini and Lila Davachi · 2013
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Open challenges for data stream mining research
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Learning with augmented class by exploiting unlabeled data
Qing Da, Yang Yu, and Zhi-Hua Zhou · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
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Fast r-cnn
Ross Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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A survey on ensemble learning for data stream classification
Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, and Albert Bifet · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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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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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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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang · 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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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Encoder based lifelong learning
Amal Rannen, Rahaf Aljundi, Matthew B Blaschko, and Tinne Tuytelaars · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2018
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Learning under concept drift: A review
Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang · 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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Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 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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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 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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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 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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Reinforced continual learning
Ju Xu and Zhanxing Zhu · 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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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Lifelong learning via progressive distillation and retrospection
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2018
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Less-forgetful learning for domain expansion in deep neural networks
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim · 2018
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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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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 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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Overcoming catastrophic forgetting for continual learning via model adaptation
Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, and Rui Yan · 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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Incremental learning using conditional adversarial networks
Ye Xiang, Ying Fu, Pan Ji, and Hua Huang · 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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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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Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
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Adaptive deep models for incremental learning: Considering capacity scalability and sustainability
Yang Yang, Da-Wei Zhou, De-Chuan Zhan, Hui Xiong, and Yuan Jiang · 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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Overcoming catastrophic forgetting with unlabeled data in the wild
Kibok Lee, Kimin Lee, Jinwoo Shin, and Honglak Lee · 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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Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, and Rama Chellappa · 2019
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Rilod: Near real-time incremental learning for object detection at the edge
Dawei Li, Serafettin Tasci, Shalini Ghosh, Jingwen Zhu, Junting Zhang, and Larry Heck · 2019
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
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Random path selection for incremental learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Continual learning by asymmetric loss approximation with single-side overestimation
Dongmin Park, Seokil Hong, Bohyung Han, and Kyoung Mu Lee · 2019
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Complementary learning for overcoming catastrophic forgetting using experience replay
Mohammad Rostami, Soheil Kolouri, and Praveen K Pilly · 2019
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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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Classification-reconstruction learning for open-set recognition
Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, and Takeshi Naemura · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff
Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S Weld, Walter S Lasecki, and Eric Horvitz · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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A new knowledge distillation for incremental object detection
Li Chen, Chunyan Yu, and Lvcai Chen · 2019
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An end-to-end architecture for class-incremental object detection with knowledge distillation
Yu Hao, Yanwei Fu, Yu-Gang Jiang, and Qi Tian · 2019
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Incremental learning techniques for semantic segmentation
Umberto Michieli and Pietro Zanuttigh · 2019
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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.
Continual lifelong learning in natural language processing: a survey
Magdalena Marta Biesialska, Katarzyna Biesialska, and Marta Ruiz Costa-Jussà · 2020
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Few-shot class-incremental learning
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong · 2020
Cited alongside, same era.
Using hindsight to anchor past knowledge in continual learning
Arslan Chaudhry, Albert Gordo, Puneet Kumar Dokania, Philip Torr, and David Lopez-Paz · 2020
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Memory-efficient incremental learning through feature adaptation
Class-incremental instance segmentation via multi-teacher networks
Yanan Gu, Cheng Deng, and Kun Wei · 2021
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Incremental few-shot instance segmentation
Dan Andrei Ganea, Bas Boom, and Ronald Poppe · 2021
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Three types of incremental learning
Gido M van de Ven, Tinne Tuytelaars, and Andreas S Tolias · 2022
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Class-incremental learning: survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartłomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost Van De Weijer · 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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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
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Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, and Cordelia Schmid · 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.
Continual learning with extended kronecker-factored approximate curvature
Janghyeon Lee, Hyeong Gwon Hong, Donggyu Joo, and Junmo Kim · 2020
Cited alongside, same era.
Class-incremental learning via deep model consolidation
Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, and C-C Jay Kuo · 2020
Cited alongside, same era.
Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
Cited alongside, same era.
Topology-preserving class-incremental learning
Xiaoyu Tao, Xinyuan Chang, Xiaopeng Hong, Xing Wei, and Yihong Gong · 2020
Cited alongside, same era.
Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer · 2020
Cited alongside, same era.
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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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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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Multi-layer perceptrons
Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, and Matthias Steinbrecher · 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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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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Augmented geometric distillation for data-free incremental person reid
Yichen Lu, Mei Wang, and Weihong Deng · 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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R-DFCIL: relation-guided representation learning for data-free class incremental learning
Qiankun Gao, Chen Zhao, Bernard Ghanem, and Jian Zhang · 2022
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Forward compatible few-shot class-incremental learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan · 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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Alleviating representational shift for continual fine-tuning
Shibo Jie, Zhi-Hong Deng, and Ziheng Li · 2022
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Gcr: Gradient coreset based replay buffer selection for continual learning
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, and Pradeep Shenoy · 2022
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Energy-based latent aligner for incremental learning
KJ Joseph, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Vineeth N Balasubramanian · 2022
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General incremental learning with domain-aware categorical representations
Jiangwei Xie, Shipeng Yan, and Xuming He · 2022
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Federated class-incremental learning
Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, and Qi Zhu · 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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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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Memory efficient continual learning with transformers
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, and Cedric Archambeau · 2022
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vclimb: A novel video class incremental learning benchmark
Andrés Villa, Kumail Alhamoud, Victor Escorcia, Fabian Caba, Juan León Alcázar, and Bernard Ghanem · 2022
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On the effectiveness of lipschitz-driven rehearsal in continual learning
Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, and Simone Calderara · 2022
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Continual learning by modeling intra-class variation
Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, and Weiyang Liu · 2022
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Adaptive orthogonal projection for batch and online continual learning
Yiduo Guo, Wenpeng Hu, Dongyan Zhao, and Bing Liu · 2022
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Anti-retroactive interference for lifelong learning
Runqi Wang, Yuxiang Bao, Baochang Zhang, Jianzhuang Liu, Wentao Zhu, and Guodong Guo · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge J. Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Unifying importance based regularisation methods for continual learning
Frederik Benzing · 2022
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Continual normalization: Rethinking batch normalization for online continual learning
Quang Pham, Chenghao Liu, and HOI Steven · 2022
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Feta: Towards specializing foundational models for expert task applications
Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Peter Staar, et al · 2022
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Energy-based models for continual learning
Shuang Li, Yilun Du, Gido van de Ven, and Igor Mordatch · 2022
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Generative negative text replay for continual vision-language pretraining
Shipeng Yan, Lanqing Hong, Hang Xu, Jianhua Han, Tinne Tuytelaars, Zhenguo Li, and Xuming He · 2022
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incdfm: Incremental deep feature modeling for continual novelty detection
Amanda Rios, Nilesh Ahuja, Ibrahima Ndiour, Utku Genc, Laurent Itti, and Omesh Tickoo · 2022
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Online continual learning through mutual information maximization
Yiduo Guo, Bing Liu, and Dongyan Zhao · 2022
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Online task-free continual learning with dynamic sparse distributed memory
Julien Pourcel, Ngoc-Son Vu, and Robert M French · 2022
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ACIL: Analytic class-incremental learning with absolute memorization and privacy protection
Huiping Zhuang, Zhenyu Weng, Hongxin Wei, Renchunzi Xie, Kar-Ann Toh, and Zhiping Lin · 2022
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Sparcl: Sparse continual learning on the edge
Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, and Jennifer Dy · 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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New insights on reducing abrupt representation change in online continual learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2022
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Alife: Adaptive logit regularizer and feature replay for incremental semantic segmentation
Youngmin Oh, Donghyeon Baek, and Bumsub Ham · 2022
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Incremental learning in semantic segmentation from image labels
Fabio Cermelli, Dario Fontanel, Antonio Tavera, Marco Ciccone, and Barbara Caputo · 2022
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Representation compensation networks for continual semantic segmentation
Chang-Bin Zhang, Jia-Wen Xiao, Xialei Liu, Ying-Cong Chen, and Ming-Ming Cheng · 2022
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Overcoming catastrophic forgetting in incremental object detection via elastic response distillation
Tao Feng, Mang Wang, and Hangjie Yuan · 2022
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Pretrained language model in continual learning: A comparative study
Tongtong Wu, Massimo Caccia, Zhuang Li, Yuan-Fang Li, Guilin Qi, and Gholamreza Haffari · 2022
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Towards continual knowledge learning of language models
Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, KIM Gyeonghun, Stanley Jungkyu Choi, and Minjoon Seo · 2022
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Lfpt5: A unified framework for lifelong few-shot language learning based on prompt tuning of t5
Chengwei Qin and Shafiq Joty · 2022
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Tomasz Korbak, Hady Elsahar, Germán Kruszewski, and Marc Dymetman · 2022
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Climb: A continual learning benchmark for vision-and-language tasks
Tejas Srinivasan, Ting-Yun Chang, Leticia Pinto Alva, Georgios Chochlakis, Mohammad Rostami, and Jesse Thomason · 2022
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Multi-view correlation distillation for incremental object detection
Dongbao Yang, Yu Zhou, Aoting Zhang, Xurui Sun, Dayan Wu, Weiping Wang, and Qixiang Ye · 2022
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Sylph: A hypernetwork framework for incremental few-shot object detection
Li Yin, Juan M Perez-Rua, and Kevin J Liang · 2022
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Static-dynamic co-teaching for class-incremental 3d object detection
Na Zhao and Gim Hee Lee · 2022
Later among the works it cites.
Incremental few-shot semantic segmentation via embedding adaptive-update and hyper-class representation
Guangchen Shi, Yirui Wu, Jun Liu, Shaohua Wan, Wenhai Wang, and Tong Lu · 2022
Later among the works it cites.
Learning multi-tasks with inconsistent labels by using auxiliary big task
Quan Feng and Songcan Chen · 2023
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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 · 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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Class-incremental learning using diffusion model for distillation and replay
Quentin Jodelet, Xin Liu, Yin Jun Phua, and Tsuyoshi Murata · 2023
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Ddgr: continual learning with deep diffusion-based generative replay
Rui Gao and Weiwei Liu · 2023
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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 · 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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Model behavior preserving for class-incremental learning
Yu Liu, Xiaopeng Hong, Xiaoyu Tao, Songlin Dong, Jingang Shi, and Yihong Gong · 2023
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Prototype-sample relation distillation: towards replay-free continual learning
Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, and Eugene Belilovsky · 2023
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Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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Ranpac: Random projections and pre-trained models for continual learning
Mark D McDonnell, Dong Gong, Amin Parveneh, Ehsan Abbasnejad, and Anton van den Hengel · 2023
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Fecam: Exploiting the heterogeneity of class distributions in exemplar-free continual learning
Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski, and Joost van de Weijer · 2023
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Few-shot class-incremental learning via training-free prototype calibration
Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, and Han-Jia Ye · 2023
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Prototype reminiscence and augmented asymmetric knowledge aggregation for non-exemplar class-incremental learning
Wuxuan Shi and Mang Ye · 2023
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Few-shot class-incremental learning by sampling multi-phase tasks
Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan · 2023
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A neural network account of memory replay and knowledge consolidation
Daniel N Barry and Bradley C Love · 2023
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Exploring continual learning of diffusion models
Michał Zając, Kamil Deja, Anna Kuzina, Jakub M Tomczak, Tomasz Trzciński, Florian Shkurti, and Piotr Miłoś · 2023
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Continual diffusion: Continual customization of text-to-image diffusion with c-lora
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, and Hongxia Jin · 2023
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Deep long-tailed learning: A survey
Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng · 2023
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Learning without forgetting for vision-language models
Da-Wei Zhou, Yuanhan Zhang, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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When prompt-based incremental learning does not meet strong pretraining
Yu-Ming Tang, Yi-Xing Peng, and Wei-Shi Zheng · 2023
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Uncertainty-aware contrastive distillation for incremental semantic segmentation
Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Moin Nabi, Xavier Alameda-Pineda, and Elisa Ricci · 2023
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Preserving locality in vision transformers for class incremental learning
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2023
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Pycil: a python toolbox for class-incremental learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, and De-Chuan Zhan · 2023
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Weakly supervised action anticipation without object annotations
Yi Zhong, Jia-Hui Pan, Haoxin Li, and Wei-Shi Zheng · 2023
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GKEAL: Gaussian kernel embedded analytic learning for few-shot class incremental task
Huiping Zhuang, Zhenyu Weng, Run He, Zhiping Lin, and Ziqian Zeng · 2023
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Online hyperparameter optimization for class-incremental learning
Yaoyao Liu, Yingying Li, Bernt Schiele, and Qianru Sun · 2023
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Computationally budgeted continual learning: What does matter?
Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K Dokania, Philip HS Torr, Ser-Nam Lim, Bernard Ghanem, and Adel Bibi · 2023
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Continual detection transformer for incremental object detection
Yaoyao Liu, Bernt Schiele, Andrea Vedaldi, and Christian Rupprecht · 2023
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Continual semantic segmentation with automatic memory sample selection
Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, and Jun Liu · 2023
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Self-training for class-incremental semantic segmentation
Lu Yu, Xialei Liu, and Joost Van de Weijer · 2023
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Endpoints weight fusion for class incremental semantic segmentation
Jia-Wen Xiao, Chang-Bin Zhang, Jiekang Feng, Xialei Liu, Joost van de Weijer, and Ming-Ming Cheng · 2023
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Preparing the future for continual semantic segmentation
Zihan Lin, Zilei Wang, and Yixin Zhang · 2023
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Principles of forgetting in domain-incremental semantic segmentation in adverse weather conditions
Tobias Kalb and Jürgen Beyerer · 2023
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Foundation model drives weakly incremental learning for semantic segmentation
Chaohui Yu, Qiang Zhou, Jingliang Li, Jianlong Yuan, Zhibin Wang, and Fan Wang · 2023
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Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning
Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, and Xi Li · 2024
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Continual learning with pre-trained models: A survey
Da-Wei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, and De-Chuan Zhan · 2024
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Expandable subspace ensemble for pre-trained model-based class-incremental learning
Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, and De-Chuan Zhan · 2024
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Multi-layer rehearsal feature augmentation for class-incremental learning
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2024
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DS-AL: A dual-stream analytic learning for exemplar-free class-incremental learning
Huiping Zhuang, Run He, Kai Tong, Ziqian Zeng, Cen Chen, and Zhiping Lin · 2024
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