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Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 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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Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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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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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc-Aurelio Ranzato · 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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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Life-long disentangled representation learning with cross-domain latent homologies
Alessandro Achille, Tom Eccles, Loic Matthey, Christopher P Burgess, Nick Watters, Alexander Lerchner, and Irina Higgins · 2018
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End-to-end incremental learning
Francisco M Castro, Manuel J Marin-Jimenez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari · 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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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2018
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Adversarial dropout for supervised and semi-supervised learning
Sungrae Park, JunKeon Park, Su-Jin Shin, and Il-Chul Moon · 2018
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Deep co-training for semi-supervised image recognition
Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, and Alan Yuille · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Cited alongside, same era.
Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
Cited alongside, same era.
Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Cited alongside, same era.
Learning to remember: A synaptic plasticity driven framework for continual learning
Oleksiy Ostapenko, Mihai Puscas, Tassilo Klein, Patrick Jahnichen, and Moin Nabi · 2019
Cited alongside, same era.
Continual unsupervised representation learning
Wcp: Worst-case perturbations for semi-supervised deep learning
Liheng Zhang and Guo-Jun Qi · 2020
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Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, and Michael Rabbat · 2021
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Rethinking experience replay: a bag of tricks for continual learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2021
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Co2l: Contrastive continual learning
Hyuntak Cha, Jaeho Lee, and Jinwoo Shin · 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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Dushyant Rao, Francesco Visin, Andrei A Rusu, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2019
Cited alongside, same era.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Cited alongside, same era.
Unsupervised progressive learning and the stam architecture
James Smith, Cameron Taylor, Seth Baer, and Constantine Dovrolis · 2019
Cited alongside, same era.
Interpolation consistency training for semi-supervised learning
Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
Cited alongside, same era.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Cited alongside, same era.
S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
Cited alongside, same era.
Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
Cited alongside, same era.
Semi-supervised class incremental learning
Alexis Lechat, Stéphane Herbin, and Frédéric Jurie · 2021
Later among the works it cites.
Memory-efficient semi-supervised continual learning: The world is its own replay buffer
James Smith, Jonathan Balloch, Yen-Chang Hsu, and Zsolt Kira · 2021
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Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning
Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, and Jun Zhu · 2021
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Dash: Semi-supervised learning with dynamic thresholding
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, and Rong Jin · 2021
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Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling
Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki · 2021
Later among the works it cites.
Class-incremental continual learning into the extended der-verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, and Simone Calderara · 2022
Closest in time.
Continual semi-supervised learning through contrastive interpolation consistency
Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, and Simone Calderara · 2022
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Debiased pseudo labeling in self-training
Baixu Chen, Junguang Jiang, Ximei Wang, Jianmin Wang, and Mingsheng Long · 2022
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Semi-supervised and unsupervised deep visual learning: A survey
Yanbei Chen, Massimiliano Mancini, Xiatian Zhu, and Zeynep Akata · 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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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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Decoupled mixup for data-efficient learning
Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, and Stan Z Li · 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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A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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