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Topology-Preserving Class-Incremental Learning
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“These results are different from those reported in [71], where TOPIC was found to have better performance.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [71]
Citations
Cites
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A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
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Metadata
- Authors
- Tao, X., Chang, X., Hong, X., Wei, X., & Gong, Y.
- Affiliations
- Xi'an Jiaotong University (CN)
- Peng Cheng Laboratory (CN)
- Corresponding author
- Gong, Yihong
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 254-270
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3105913416
- Cited by
- 162
- References
- 49
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 38 · 2023: 39 · 2022: 25 · 2021: 27 · 2020: 2
- FWCI
- 8.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- class-incremental learning, catastrophic forgetting, topology preservation, continual learning
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