CIELO: Class-Incremental Continual Learning for Overcoming Catastrophic Forgetting With Smartphone-Based Indoor Localization
With dynamically evolving indoor environments, class-incremental learning (CIL) plays a crucial role in enabling indoor localization systems to adapt to new indoor areas. However, CIL poses additional challenges such as catastrophic forgetting, where patterns from previously learned paths are overwr...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IEEE
2025-01-01
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| シリーズ: | IEEE Access |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/10965685/ |
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