Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

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2020 Preprint Clear all

2020
Discriminative Representation Loss (DRL): A More Efficient Approach Than Gradient Re-projection in continual learning.Yu Chen, Tom Diethe, Peter A. FlachPreprint
2020
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint.Dong Yin, Mehrdad Farajtabar, Ang Li … Alex MottPreprint
2020
Author response: Can sleep protect memories from catastrophic forgetting?Oscar C. González, Yury Sokolov, Giri P. Krishnan … Maxim BazhenovPreprint · University of California San Diego
2020
Combining Variational Continual Learning with FiLM LayersNoel Loo, S. Swaroop, Richard E. TurnerPreprint
2020
Continual Learning Using Multi-view Task Conditional Neural NetworksHonglin Li, Payam M. Barnaghi, Shirin Enshaeifar, F. GanzPreprint
2020
Mitigating the catastrophic forgetting effect using generative modelsDi Torino, E. FicarraPreprint
2020
Neural Architectures Design and Search for Deep Learning and Continual Learning.Xilai LiPreprint
2020
Safety-Oriented Stability Biases for Continual LearningAshish Gaurav, Jaeyoung Lee, Vahdat Abdelzad, Sachin VernekarPreprint
2020
An Application-Independent Approach to Building Task-Oriented Chatbots with Interactive Continual LearningS. Mazumder, Bing Liu, Shuai Wang, Sepideh EsmaeilpourPreprint
2020
Chaotic Continual LearningTouraj Laleh, Mojtaba Faramarzi, I. Rish, Sarath ChandarPreprint
2020
Logical Composition for Lifelong Reinforcement LearningGeraud Nangue Tasse, Steven James, Benjamin RosmanPreprint
2020
Active Continual Learning for Planning and NavigationA. H. Qureshi, Yinglong Miao, Michael C. YipPreprint
2020
Can Expressive Posterior Approximations Improve Variational Continual Learning?S. Auddy, Jakob J. Hollenstein, Matteo Saveriano … J. PiaterPreprint
2020
A General Framework for Continual Learning of Compositional StructuresJorge Armando Mendez Mendez, Eric EatonPreprint
2020
Appendices to “A General Framework for Continual Learning of Compositional Structures”Jorge Armando Mendez MendezPreprint
2020
Continual learning for efficient machine learningArslan ChaudhryPreprint
2020
Continual Learning with Neural NetworksS. EbrahimiPreprint
2020
Discriminative representation loss (DRL) for continual learning: A solution for CLVision challengeYu Chen, Jia Jun Ma, Hanyu Wan … Tom DiethePreprint
2020
Incremental Learning with Bayesian Neural NetworksPolitecnico di Torino, E. FicarraPreprint
2020
INTEGRATING NEW KNOWLEDGE WITHOUT CATASTROPHIC INTERFERENCE: COMPUTATIONAL AND THEORETICAL INVESTIGATIONS IN A HIERARCHICALLY STRUCTURED ENVIRONMENTJames L. McClelland, L. SternPreprint
2020
Learning Without Forgetting Approaches for Lifelong Robotic VisionZhengwei Wang, Eoin Brophy, T. WardPreprint
2020
Lifelong Learning of Neural Networks: Detecting Novelty and Adapting to New Domains without ForgettingMarc MasanaPreprint
2020
Robust Deep Learning in the Open World with Lifelong Learning and Representation LearningKibok LeePreprint
2020
Supplementary Material for Few-Shot Class-Incremental LearningXiaoyu Tao, Xiaopeng Hong, Xinyuan Chang … Peng ChengPreprint

About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.