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[2602.20911] From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning
Summary
The paper presents the Semantic-guided Adaptive Expert Forest (SAEF), a novel approach for Class-Incremental Learning (CIL) that enhances knowledge sharing by organizing task adapters into a structured hierarchy, achieving state-of-the-art performance on benchmark datasets.
Why It Matters
As machine learning models increasingly need to adapt to new classes without forgetting previous knowledge, the SAEF method addresses a critical challenge in CIL. By leveraging semantic relationships among tasks, it improves model efficiency and performance, making it relevant for researchers and practitioners in AI and machine learning.
Key Takeaways
- SAEF organizes task adapters into a structured hierarchy for better knowledge sharing.
- The method groups tasks based on semantic relationships, enhancing learning efficiency.
- SAEF achieves state-of-the-art performance in Class-Incremental Learning tasks.
Computer Science > Machine Learning arXiv:2602.20911 (cs) [Submitted on 24 Feb 2026] Title:From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning Authors:Ruiqi Liu, Boyu Diao, Hangda Liu, Zhulin An, Fei Wang, Yongjun Xu View a PDF of the paper titled From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning, by Ruiqi Liu and 5 other authors View PDF HTML (experimental) Abstract:Class-Incremental Learning (CIL) requires models to learn new classes without forgetting old ones. A common method is to freeze a pre-trained model and train a new, lightweight adapter for each task. While this prevents forgetting, it treats the learned knowledge as a simple, unstructured collection and fails to use the relationships between tasks. To this end, we propose the Semantic-guided Adaptive Expert Forest (SAEF), a new method that organizes adapters into a structured hierarchy for better knowledge sharing. SAEF first groups tasks into conceptual clusters based on their semantic relationships. Then, within each cluster, it builds a balanced expert tree by creating new adapters from merging the adapters of similar tasks. At inference time, SAEF finds and activates a set of relevant experts from the forest for any given input. The final prediction is made by combining the outputs of these activated experts, weighted by how confident each expert is. Experiments on sev...