HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
China Mobile Research Institute · Beijing University of Posts and Telecommunications · Nanyang Technological University, Singapore · Beijing University of Aeronautics and Astronautics · China Mobile Communications Company Limited Research Institute
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摘要
Domain Incremental Learning is a critical scenario that requires models to continuously adapt to new data domains without retraining. However, domain shifts often cause severe performance degradation. To address this, we propose Hybrid Energy-Distance Prompt, a domain-incremental framework inspired by Helmholtz free energy. HEDP introduces an energy regularization loss to enhance the separability of domain representations and a hybrid energy-distance weighted mechanism that fuses energy-based and distance-based cues to improve domain selection and generalization. Experiments on multiple benchmarks, including CORe50, show that HEDP achieves superior performance on unseen domains with a 2.57\% accuracy gain, effectively mitigating catastrophic forgetting and enhancing open-world adaptability. Our code is \href{https://anonymous.4open.science/r/HEDP-C879/}{available here}.