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Long-Tailed Class Incremental Learning (LT-CIL)
Updated 4 July 2026
- LT-CIL is a learning paradigm that addresses imbalanced class distributions and evolving datasets by incrementally adding new categories.
- It employs methodologies like transfer learning, few-shot learning, and memory replay to mitigate forgetting and balance data representation.
- Recent benchmark studies demonstrate LT-CIL’s potential in real-world applications such as computer vision and natural language processing.
Searching arXiv for LT-CIL papers to ground the article in recent work. Searching for core LT-CIL benchmark paper.