Cited by 1,091Domain Adaptation and Few-Shot LearningData Stream Mining TechniquesAdvanced Neural Network Applications

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Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

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Chaudhry, Arslan, Dokania, Puneet K., Ajanthan, Thalaiyasingam, Torr, Philip H. S.·2018·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-01252-6_33

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Authors: Chaudhry, A., Dokania, P. K., Ajanthan, T., & Torr, P. H. S.
Affiliations: University of Oxford (GB)
Corresponding author: Ajanthan, Thalaiyasingam
ORCID: Ajanthan, Thalaiyasingam
Torr, Philip H. S.
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2018
Pages: 556-572
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-01252-6_33
OpenAlex: W2786446225
Cited by: 1,091
References: 33
Citations by year: 2026: 78 · 2025: 182 · 2024: 217 · 2023: 189 · 2022: 135 · 2021: 154 · 2020: 84 · 2019: 45 · 2018: 7
FWCI: 29.59
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Engineering and Physical Sciences Research Council (EP/N019474/1, EP/M013774/1)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: incremental learning, catastrophic forgetting, intransigence, path integral, Kullback-Leibler divergence, knowledge preservation, classifier updating

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Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

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