English(EN) HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning

HEDP框架使用能量-距离提示进行域增量学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员推出了一种新的域增量学习框架HEDP，旨在防止模型适应新数据时性能下降。该框架采用混合方法，结合了基于能量和基于距离的机制，灵感来自亥姆霍兹自由能。实验表明，HEDP在未见过的数据域上实现了2.57%的准确率提升，有效减少了灾难性遗忘并增强了适应性。 AI

影响引入了一种新颖的域增量学习方法，有望提高模型适应性并减少不断变化的数据环境中的性能下降。

排序理由这是一篇详细介绍域增量学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Yu Feng, Zhen Tian, Haoran Luo, Xie Yu, Diancheng Cheng, Haoyue Zheng, Shuai Lyu, Ping Zong, Lianyuan Li, Xin Ge, Yifan Zhu · 2026-05-08 04:00

HEDP：一种基于混合能量-距离提示的领域增量学习框架

arXiv:2605.05776v1 Announce Type: new 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 Hyb…