HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
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arXiv
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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918487077683200 |
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| author | Feng, Yu Tian, Zhen Luo, Haoran Yu, Xie Cheng, Diancheng Zheng, Haoyue Lyu, Shuai Zong, Ping Li, Lianyuan Ge, Xin Zhu, Yifan |
| author_facet | Feng, Yu Tian, Zhen Luo, Haoran Yu, Xie Cheng, Diancheng Zheng, Haoyue Lyu, Shuai Zong, Ping Li, Lianyuan Ge, Xin Zhu, Yifan |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05776 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning Feng, Yu Tian, Zhen Luo, Haoran Yu, Xie Cheng, Diancheng Zheng, Haoyue Lyu, Shuai Zong, Ping Li, Lianyuan Ge, Xin Zhu, Yifan Artificial Intelligence 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}. |
| title | HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.05776 |