Domain-Incremental Learning

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
May 7, 2026Yu Feng, Zhen Tian, Haoran Luo +8Prompt Learning Domain-Incremental Learning
China Mobile Research Institute, China · Beijing University of Posts and Telecommunications, China · Nanyang Technological University, Singapore +1
A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability
May 5, 2026Ryan King, Conrad Krueger, Ethan Veselka +2Clinical Outcome Prediction Domain-Incremental Learning
Computer Science & Engineering, Texas A&M University, College Station, United States
MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
May 5, 2026Shishir Muralidhara, Didier Stricker, René SchusterDomain-Incremental Learning Incremental Learning
German Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany · University of Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Str. 47, 67663 Kaiserslautern, Germany
Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting
Apr 20, 2026Hyeonseo Jang, Hyuk Kwon, Kibok LeeClass-Incremental Learning Vision-Language Models
Yonsei University
Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments
Apr 10, 2026Xingyu Shao, Zhiqiang Yan, Liangzheng Sun +5Cross-View Geo-Localization Visual Place Recognition
ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
Apr 3, 2026Jiekai Wu, Rong Fu, Chuangqi Li +10Class-Incremental Learning Image Segmentation
Independent Researcher · The Institute of Collaborative Innovation, University of Macau, Macau 999078, China · Department of Information and Computing Sciences, Faculty of Science, Utrecht University, Utrecht 3584 CC, Netherlands +10

May 7, 2026, cs.AIJ/K move · Enter open · S save

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

Yu Feng, Zhen Tian, Haoran Luo, Xie Yu+7

China Mobile Research Institute, China · Beijing University of Posts and Telecommunications, China · Nanyang Technological University, Singapore+1

Prompt Learning Domain-Incremental Learning Cross-Domain Generalization Incremental Learning Catastrophic Forgetting

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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 available here.

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Latest papers 36

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

A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability

MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities

Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting

Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments

ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow