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The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Hypernetworks
David Ha, Andrew Dai, and Quoc V Le · 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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Matching networks for one shot learning
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Semi-supervised deep continuous learning
Michael Baucum, Daniel Belotto, Sayre Jeannet, Eric Savage, Prannoy Mupparaju, and Carlos W Morato · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 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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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, and Richard S Zemel · 2018
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Learning to self-train for semi-supervised few-shot classification
Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, and Bernt Schiele · 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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Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Incremental few-shot learning via vector quantization in deep embedded space
Kuilin Chen and Chi-Guhn Lee · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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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
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Automatically discovering and learning new visual categories with ranking statistics
Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, and Andrew Zisserman · 2020
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Self-supervised label augmentation via input transformations
Hankook Lee, Sung Ju Hwang, and Jinwoo Shin · 2020
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Class-incremental learning: survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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When does self-supervision improve few-shot learning?
Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan · 2020
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Few-shot class-incremental learning
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
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Sharp-maml: Sharpness-aware model-agnostic meta learning
Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen · 2022
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Subspace regularizers for few-shot class incremental learning
Afra Feyza Akyürek, Ekin Akyürek, Derry Wijaya, and Jacob Andreas · 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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Queried unlabeled data improves and robustifies class-incremental learning
Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, and Zhangyang Wang · 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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Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, and C-C Jay Kuo · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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Hypernetworks for continual semi-supervised learning
Dhanajit Brahma, Vinay Kumar Verma, and Piyush Rai · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Semantic-aware knowledge distillation for few-shot class-incremental learning
Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mehrtash Harandi · 2021
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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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Self-supervised training enhances online continual learning
Jhair Gallardo, Tyler L Hayes, and Christopher Kanan · 2021
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Continually learning self-supervised representations with projected functional regularization
Alex Gomez-Villa, Bartlomiej Twardowski, Lu Yu, Andrew D Bagdanov, and Joost van de Weijer · 2022
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Note: Robust continual test-time adaptation against temporal correlation
Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Constrained few-shot class-incremental learning
Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi · 2022
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Learning from limited and imperfect data (l2id)
L2ID · 2022
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Learning to predict gradients for semi-supervised continual learning
Yan Luo, Yongkang Wong, Mohan Kankanhalli, and Qi Zhao · 2022
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Representational continuity for unsupervised continual learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, and Sung Ju Hwang · 2022
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Towards robust and reproducible active learning using neural networks
Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, and Shadab Khan · 2022
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The challenges of continuous self-supervised learning
Senthil Purushwalkam, Pedro Morgado, and Abhinav Gupta · 2022
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Better self-training for image classification through self-supervision
Attaullah Sahito, Eibe Frank, and Bernhard Pfahringer · 2022
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Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
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A survey on deep semi-supervised learning
Xiangli Yang, Zixing Song, Irwin King, and Zenglin Xu · 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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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Zhang Xingxing, Su Hang, and Zhu Jun · 2023
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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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