Fetching the paper…
Reading the bibliography…
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones.
Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson · 1901
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
Detecting faces in images: A survey
Ming-Hsuan Yang · 2002
Earlier work this paper cites.
Multimodal human–computer interaction: A survey
Alejandro Jaimes and Nicu Sebe · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control
Stephen T Grossberg · 2012
Earlier work this paper cites.
The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects
Martial Mermillod, Aurélia Bugaiska, and Patrick Bonin · 2013
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
A survey on ensemble learning for data stream classification
Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, and Albert Bifet · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
Cross-modality binary code learning via fusion similarity hashing
Hong Liu, Rongrong Ji, Yongjian Wu, Feiyue Huang, and Baochang Zhang · 2017
Earlier work this paper cites.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Earlier work this paper cites.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Earlier work this paper cites.
Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
Earlier work this paper cites.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Earlier work this paper cites.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Earlier work this paper cites.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
Earlier work this paper cites.
Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
Earlier work this paper cites.
Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, and Rama Chellappa · 2019
Earlier work this paper cites.
Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Earlier work this paper cites.
Learning to reduce dual-level discrepancy for infrared-visible person re-identification
Zhixiang Wang, Zheng Wang, Yinqiang Zheng, Yung-Yu Chuang, and Shin’ichi Satoh · 2019
Earlier work this paper cites.
Pytorch image models
Ross Wightman · 2019
Earlier work this paper cites.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Earlier work this paper cites.
A large-scale study of representation learning with the visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
Earlier work this paper cites.
Memory-efficient incremental learning through feature adaptation
Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, and Cordelia Schmid · 2020
Earlier work this paper cites.
Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
Earlier work this paper cites.
Understanding the role of training regimes in continual learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, and Hassan Ghasemzadeh · 2020
Earlier work this paper cites.
Incremental few-shot object detection
Juan-Manuel Perez-Rua, Xiatian Zhu, Timothy M Hospedales, and Tao Xiang · 2020
Earlier work this paper cites.
Few-shot class-incremental learning
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong · 2020
Earlier work this paper cites.
Co-tuning for transfer learning
Kaichao You, Zhi Kou, Mingsheng Long, and Jianmin Wang · 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.
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.
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.
I3dol: Incremental 3d object learning without catastrophic forgetting
Jiahua Dong, Yang Cong, Gan Sun, Bingtao Ma, and Lichen Wang · 2021
Cited alongside, same era.
Few-shot class-incremental learning via relation knowledge distillation
Foster: Feature boosting and compression for class-incremental learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2022
Later among the works it cites.
S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
Yabin Wang, Zhiwu Huang, and Xiaopeng Hong · 2022
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Coca: Contrastive captioners are image-text foundation models
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong · 2021
Cited alongside, same era.
Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, et al · 2021
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2021
Cited alongside, same era.
Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2021
Cited alongside, same era.
Distilling causal effect of data in class-incremental learning
Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, and Hanwang Zhang · 2021
Cited alongside, same era.
Ib-drr-incremental learning with information-back discrete representation replay
Jian Jiang, Edoardo Cetin, and Oya Celiktutan · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel · 2022
Later among the works it cites.
Benchmarking omni-vision representation through the lens of visual realms
Yuanhan Zhang, Zhenfei Yin, Jing Shao, and Ziwei Liu · 2022
Later among the works it cites.
Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu · 2022
Later among the works it cites.
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
Later among the works it cites.
Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
Later among the works it cites.
Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Later among the works it cites.
Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Later among the works it cites.
Alternating gradient descent and mixture-of-experts for integrated multimodal perception
Hassan Akbari, Dan Kondratyuk, Yin Cui, Rachel Hornung, Huisheng Wang, and Hartwig Adam · 2023
Closest in time.
A structural developmental neural network with information saturation for continual unsupervised learning
Zhiyong Ding, Haibin Xie, Peng Li, and Xin Xu · 2023
Closest in time.
Heterogeneous forgetting compensation for class-incremental learning
Jiahua Dong, Wenqi Liang, Yang Cong, and Gan Sun · 2023
Closest in time.
A unified continual learning framework with general parameter-efficient tuning
Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi, Gang Zhang, Bernard Ghanem, and Jian Zhang · 2023
Closest in time.
Ddgr: continual learning with deep diffusion-based generative replay
Rui Gao and Weiwei Liu · 2023
Closest in time.
Fact: Factor-tuning for lightweight adaptation on vision transformer
Shibo Jie and Zhi-Hong Deng · 2023
Closest in time.
Generating instance-level prompts for rehearsal-free continual learning
Dahuin Jung, Dongyoon Han, Jihwan Bang, and Hwanjun Song · 2023
Closest in time.
Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2023
Closest in time.
How to configure good in-context sequence for visual question answering
Li Li, Jiawei Peng, Huiyi Chen, Chongyang Gao, and Xu Yang · 2023
Closest in time.
Dpps: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services
Long Li, Jianbo Huang, Liang Chang, Jian Weng, Jia Chen, and Jingjing Li · 2023
Closest in time.
Large-scale pre-trained models are surprisingly strong in incremental novel class discovery
Mingxuan Liu, Subhankar Roy, Zhun Zhong, Nicu Sebe, and Elisa Ricci · 2023
Closest in time.
Rf-badge: Vital sign-based authentication via rfid tag array on badges
Jingyi Ning, Lei Xie, Chuyu Wang, Yanling Bu, Fengyuan Xu, Da-Wei Zhou, Sanglu Lu, and Baoliu Ye · 2023
Closest in time.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
Closest in time.
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
Closest in time.
Pilot: A pre-trained model-based continual learning toolbox
Hai-Long Sun, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2023
Closest in time.
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
Closest in time.
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
Closest in time.
A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
Closest in time.
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
Closest in time.
Isolation and impartial aggregation: A paradigm of incremental learning without interference
Yabin Wang, Zhiheng Ma, Zhiwu Huang, Yaowei Wang, Zhou Su, and Xiaopeng Hong · 2023
Closest in time.
Slca: Slow learner with classifier alignment for continual learning on a pre-trained model
Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen, and Yunchao Wei · 2023
Closest in time.
Vision-audio fusion slam in dynamic environments
Tianwei Zhang, Huayan Zhang, and Xiaofei Li · 2023
Closest in time.
Preserving locality in vision transformers for class incremental learning
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2023
Closest in time.
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
Closest in time.
Single depth image 3d face reconstruction via domain adaptive learning
Xiaoxu Cai, Jianwen Lou, Jiajun Bu, Junyu Dong, Haishuai Wang, and Hui Yu · 2024
Closest in time.
Steering prototypes with prompt-tuning for rehearsal-free continual learning
Zhuowei Li, Long Zhao, Zizhao Zhang, Han Zhang, Di Liu, Ting Liu, and Dimitris N Metaxas · 2024
Closest in time.
Moirétracker: Continuous camera-to-screen 6-dof pose tracking based on moiré pattern
Jingyi Ning, Lei Xie, Yi Li, Yingying Chen, Yanling Bu, Chuyu Wang, Sanglu Lu, and Baoliu Ye · 2024
Closest in time.
Create your world: Lifelong text-to-image diffusion
Gan Sun, Wenqi Liang, Jiahua Dong, Jun Li, Zhengming Ding, and Yang Cong · 2024
Closest in time.
Exploring diverse in-context configurations for image captioning
Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, and Xin Geng · 2024
Closest in time.
Multi-layer rehearsal feature augmentation for class-incremental learning
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2024
Closest in time.
Continual learning with pre-trained models: A survey
Da-Wei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, and De-Chuan Zhan · 2024
Closest in time.
Class-incremental learning: A survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2024
Closest in time.