ADAPTIVE SIMILARITY-DRIVEN APPROACHES FOR CONTINUAL LEARNING: BRIDGING TASK-AWARE AND TASK-FREE PARADIGMS

Continual learning aims to enable models to learn sequential tasks without forgetting previously acquired knowledge. This paper presents an adaptive similarity-driven framework that bridges the gap between task-aware and task-free paradigms in continual learning. By leveraging similarity metrics to dynamically adjust learning strategies based on incoming data distributions, the proposed approach allows models to maintain performance across tasks without relying on explicit task boundaries. Experimental evaluations on benchmark datasets demonstrate that the adaptive similarity-driven method outperforms traditional task-aware and task-free models in mitigating catastrophic forgetting while preserving scalability. The findings offer a promising direction for developing flexible and efficient continual learning systems adaptable to real-world scenarios.