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Continual learning (CL) aims to help deep neural networks learn new knowledge while retaining what has been learned.
Stochastic differential equations
Bernt Øksendal and Bernt Øksendal · 2003
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Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper)
Andrew Gelman · 2004
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The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
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A visual vocabulary for flower classification
M-E Nilsback and Andrew Zisserman · 2006
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Herding dynamical weights to learn
Max Welling · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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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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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
Takaya Saito and Marc Rehmsmeier · 2015
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 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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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 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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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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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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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 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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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Towards robust evaluations of continual learning
Sebastian Farquhar and Yarin Gal · 2018
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Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh · 2018
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Empirical evaluation of neural process objectives
Tuan Anh Le, Hyunjik Kim, Marta Garnelo, Dan Rosenbaum, Jonathan Schwarz, and Yee Whye Teh · 2018
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On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
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Model selection of bayesian hierarchical mixture of experts based on variational inference
Yuji Iikubo, Shunsuke Horii, and Toshiyasu Matsushima · 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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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Cited alongside, same era.
Continual deep learning by functional regularisation of memorable past
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, and Mohammad Emtiyaz E Khan · 2020
Cited alongside, same era.
Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
Tip-adapter: Training-free adaption of clip for few-shot classification
Renrui Zhang, Wei Zhang, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
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Towards exemplar-free continual learning in vision transformers: An account of attention, functional and weight regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello, Bogdan Raducanu, and Joost van de Weijer · 2022
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Out-of-distribution detection in unsupervised continual learning
Jiangpeng He and Fengqing Zhu · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 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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Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Continual learning in sensor-based human activity recognition: An empirical benchmark analysis
Saurav Jha, Martin Schiemer, Franco Zambonelli, and Juan Ye · 2021
Cited alongside, same era.
Clip-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2021
Cited alongside, same era.
Continual learning via local module composition
Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, and Laurent Charlin · 2021
Cited alongside, same era.
Regularization shortcomings for continual learning, 2021
Timothee LESORT and Andrei Stoian · 2021
Cited alongside, same era.
Learning bayesian sparse networks with full experience replay for continual learning
Qingsen Yan, Dong Gong, Yuhang Liu, Anton van den Hengel, and Javen Qinfeng Shi · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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Priors in bayesian deep learning: A review
Vincent Fortuin · 2022
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Multi-task processes
Donggyun Kim, Seongwoong Cho, Wonkwang Lee, and Seunghoon Hong · 2022
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NPCL: Neural processes for uncertainty-aware continual learning
Saurav Jha, Dong Gong, He Zhao, and Lina Yao · 2023
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Bayesian prompt learning for image-language model generalization
Mohammad Mahdi Derakhshani, Enrique Sanchez, Adrian Bulat, Victor G Turrisi da Costa, Cees GM Snoek, Georgios Tzimiropoulos, and Brais Martinez · 2023
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Attriclip: A non-incremental learner for incremental knowledge learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang, Jianzhuang Liu, Shaohui Lin, Songcen Xu, Jinhu Lü, and Baochang Zhang · 2023
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Maple: Multi-modal prompt learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2023
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Bayesian posterior approximation with stochastic ensembles
Oleksandr Balabanov, Bernhard Mehlig, and Hampus Linander · 2023
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From categories to classifier: Name-only continual learning by exploring the web
Ameya Prabhu, Hasan Abed Al Kader Hammoud, Ser-Nam Lim, Bernard Ghanem, Philip HS Torr, and Adel Bibi · 2023
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Combined scaling for zero-shot transfer learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, et al · 2023
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Continual vision-language representaion learning with off-diagonal information, 2023
Zixuan Ni, Longhui Wei, Siliang Tang, Yueting Zhuang, and Qi Tian · 2023
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Language-aware soft prompting for vision & language foundation models, 2023
Adrian Bulat and Georgios Tzimiropoulos · 2023
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Continual evaluation for lifelong learning: Identifying the stability gap
Matthias De Lange, Gido M van de Ven, and Tinne Tuytelaars · 2023
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Overcoming the stability gap in continual learning
Md Yousuf Harun and Christopher Kanan · 2023
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Preventing zero-shot transfer degradation in continual learning of vision-language models
Z. Zheng, M. Ma, K. Wang, Z. Qin, X. Yue, and Y. You · 2023
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Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2023
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What does a platypus look like? generating customized prompts for zero-shot image classification
Sarah Pratt, Ian Covert, Rosanne Liu, and Ali Farhadi · 2023
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I2i: Initializing adapters with improvised knowledge
Tejas Srinivasan, Furong Jia, Mohammad Rostami, and Jesse Thomason · 2023
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Just say the name: Online continual learning with category names only via data generation
Minhyuk Seo, Diganta Misra, Seongwon Cho, Minjae Lee, and Jonghyun Choi · 2024
Closest in time.
Saurav Jha, Shiqi Yang, Masato Ishii, Mengjie Zhao, Christian Simon, Muhammad Jehanzeb Mirza, Dong Gong, Lina Yao, Shusuke Takahashi, and Yuki Mitsufuji · 2024
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Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models
Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yunpeng Chen, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, et al · 2024
Closest in time.
Task vectors are cross-modal
Grace Luo, Trevor Darrell, and Amir Bar · 2024
Closest in time.
Youngjae Cho, HeeSun Bae, Seungjae Shin, Yeo Dong Youn, Weonyoung Joo, and Il-Chul Moon · 2024
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Learning to prompt with text only supervision for vision-language models
Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Muzammal Naseer, Luc Van Gool, and Federico Tombari · 2024
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