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arXiv · 2605.05776

HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning

Yu Feng·Zhen Tian·Haoran Luo·Xie Yu·Diancheng Cheng·Haoyue Zheng·Shuai Lyu·Ping Zong·Lianyuan Li·Xin Ge·Yifan Zhu
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Abstract

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://github.com/dannis97500/HEDP/}{available here}.

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Yu Feng, Zhen Tian, Haoran Luo, Xie Yu, Diancheng Cheng, Haoyue Zheng, Shuai Lyu, Ping Zong, Lianyuan Li, Xin Ge, Yifan Zhu. 2026-05-07. HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning. https://arxiv.org/abs/2605.05776

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