Fetching the paper…
Reading the bibliography…
We present the One Pass ImageNet (OPIN) problem, which aims to study the effectiveness of deep learning in a streaming setting.
Random sampling with a reservoir
Jeffrey S. Vitter · 1985
Earlier work this paper cites.
On biased reservoir sampling in the presence of stream evolution
Charu C. Aggarwal · 2006
Earlier work this paper cites.
On the generalization ability of on-line learning algorithms
N. Cesa-Bianchi, A. Conconi, and C. Gentile · 2006
Earlier work this paper cites.
Online gradient descent learning algorithms
Yiming Ying and Massimiliano Pontil · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Exponential reservoir sampling for streaming language models
Miles Osborne, Ashwin Lall, and Benjamin Van Durme · 2014
Earlier work this paper cites.
Online learning as stochastic approximation of regularization paths: Optimality and almost-sure convergence
Pierre Tarrès and Yuan Yao · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
Earlier work this paper cites.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Earlier work this paper cites.
Non-parametric stochastic approximation with large step sizes, 2016
Aymeric Dieuleveut and Francis Bach · 2016
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
Cited alongside, same era.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
Cited alongside, same era.
Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 2018
Cited alongside, same era.
End-to-end incremental learning
Francisco M. Castro, Manuel J. Marin-Jimenez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari · 2018
Cited alongside, same era.
Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
Cited alongside, same era.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, and Greg Wayne · 2019
Coresets via bilevel optimization for continual learning and streaming
Zalán Borsos, Mojmír Mutnỳ, and Andreas Krause · 2020
Later among the works it cites.
Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
Later among the works it cites.
Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
Later among the works it cites.
Challenges in benchmarking stream learning algorithms with real-world data
Vinicius M. A. Souza, Denis M. dos Reis, André Gustavo Maletzke, and Gustavo E. A. P. A. Batista · 2020
Later among the works it cites.
Class-incremental learning: survey and performance evaluation on image classification, 2021
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, and Joost van de Weijer · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Cited alongside, same era.
Machine learning for streaming data: state of the art, challenges, and opportunities
Heitor Murilo Gomes, Jesse Read, Albert Bifet, Jean Paul Barddal, and João Gama · 2019
Cited alongside, same era.
Rethinking experience replay: a bag of tricks for continual learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello, and Simone Calderara · 2020
Cited alongside, same era.
Optimization and generalization of regularization-based continual learning: a loss approximation viewpoint
Dong Yin, Mehrdad Farajtabar, Ang Li, Nir Levine, and Alex Mott · 2020
Cited alongside, same era.
Yogesh Balaji, Mehrdad Farajtabar, Dong Yin, Alex Mott, and Ang Li · 2021
Closest in time.
Online continual learning with natural distribution shifts: An empirical study with visual data
Zhipeng Cai, Ozan Sener, and Vladlen Koltun · 2021
Closest in time.
Task-agnostic continual learning with hybrid probabilistic models
Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, and Razvan Pascanu · 2021
Closest in time.
Rainbow memory: Continual learning with a memory of diverse samples
Jihwan Bang, Heesu Kim, Youngjoon Yoo, Jung-Woo Ha, and Jonghyun Choi · 2021
Closest in time.
Using hindsight to anchor past knowledge in continual learning
Arslan Chaudhry, Albert Gordo, Puneet K Dokania, Philip Torr, and David Lopez-Paz · 2021
Closest in time.