Domain-Incremental Learning

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Domain-incremental learning studies how a model can keep performing the same task as its input distribution changes, such as across locations, devices, environments, or time. Common approaches replay selected examples, constrain or distill updates to protect prior knowledge, or isolate new domain-specific parameters with adapters, prompts, or expert models. Other methods use prototypes or routing to select domain-specific knowledge, while memory-free approaches adapt frozen foundation models or preserve feature structure. The central challenge is balancing adaptation to new domains against forgetting old ones, especially when domain boundaries are unclear and storage, computation, or access to past data is limited.

Latest papers

55 papers6 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Episodic looking - using reinforcement learning to bias perception enhances few-shot and continual learning with protection from forgetting
Oct 28, 2026David Rawlinson, Gideon KowadloContinual Learning Catastrophic Forgetting
Cerenaut Melbourne Australia
WiMoT: Replay-Free Continual Wi-Fi Sensing via Moment Transport
Oct 23, 2026Chunchun Yang, Yang Cao, Zhitang ChenContinual Learning Wi-Fi Sensing
School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China · Independent Researcher
APRIL: Ambiguity-Aware Expert Routing for Respiratory Sound Domain-Incremental Learning
Oct 20, 2026Xinhong Li, Weiping TuRespiratory Sound Classification Domain-Incremental Learning
School of Computer Science, Wuhan University, Wuhan, Hubei, China
Continual Learning without Continual Training
Oct 7, 2026Nikita Narayanan, Ritham Majumdarr, Sonali ParbhooContinual Learning Class-Incremental Learning
Imperial College London
Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts
Oct 2, 2026Yue Hou, Ruomei Liu, Yingke Su +2Continual Test-Time Adaptation Domain-Incremental Learning
State Key Laboratory of Complex & Critical Software Environment, Beihang University · Shen Yuan Honors College, Beihang University
Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time
Sep 29, 2026Jae-Ho Lee, Min-Yeong Park, Jun-Yeong Moon +2Class-Incremental Learning Domain-Incremental Learning
Korea University, Seoul, Republic of Korea · Kyung Hee University, Yongin, Republic of Korea
Domain-Incremental Learning for Generative Speech Enhancement
Sep 28, 2026Manjunath Mulimani, Annamaria Mesaros, Minje Kim +1Domain-Incremental Learning Speech Enhancement
Aalborg University, Department of Electronic Systems, Denmark · Tampere University, Signal Processing Research Centre, Finland · University of Illinois Urbana-Champaign, Siebel School of Computing and Data Science, USA
Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning
Sep 26, 2026Nanxi Yu, Kang Li, Ye Du +3Ophthalmic Image Analysis Domain-Incremental Learning
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, China · Department of Biomedical Engineering and Sports Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China · School of Future Technology, South China University of Technology, Guangdong, China +1
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
Sep 21, 2026Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel +3Intelligent Transportation Systems Mixture of Experts
ETH Zurich, Zurich, Switzerland. · Bosch Research, Hildesheim, Germany. · INSAIT, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria.
ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks
Sep 16, 2026Rupesh Raj Karn, Johann Knechtel, Ozgur SinanogluGraph Neural Networks Domain-Incremental Learning
Center for Cyber Security, New York University, Abu Dhabi, UAE.
MCLC-NET: Multimodal Continual Learning for Leaf Counting
Sep 16, 2026Ruchi Bhatt, Pratibha Kumari, Shreya Bansal +3Plant Phenotyping Object Counting
Department of Computer Science & Engineering, Indian Institute of Technology Ropar, Punjab, India · Department of Informatics and Data Science, University of Regensburg, Regensburg, Germany · Department of Computer Science, Khalifa University, UAE
Parameter isolation with domain-specific experts for incremental audio classification
Sep 13, 2026Jongyeon Park, Do-Hyeon Lim, Sang-won Park +4Domain-Incremental Learning Audio Classification
Dept. of AI Convergence, Gwangju Institute of Science and Technology (GIST) · Dept. of EECS, Gwangju Institute of Science and Technology (GIST) · AunionAI Co., Ltd., Gwangju, Korea +1
Domain-Incremental Learning for Multi-Channel Replay Speech Detection
Sep 10, 2026Michael NeriSpeech Processing Domain-Incremental Learning
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
Restoring Without Forgetting: Continual Learning Across Image Degradations
Aug 24, 2026Alif Ashrafee, Bartosz KrawczykAdapter-Based Continual Learning Image Restoration
Rochester Institute of Technology New York, USA
Mind the Context: Continual Learning of Socially Appropriate Robot Actions via Environmental-Social Disentanglement
Aug 13, 2026Rafal Robert Karpinski, Fethiye Irmak Dogan, Nikhil Churamani +4Disentangled Representation Learning Continual Robot Learning
Utrecht University, Utrecht, The Netherlands · Department of Computer Science and Technology, University of Cambridge, UK
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Aug 11, 2026Qiang Wang, Songlin Dong, Shaokun Wang +5Domain-Incremental Learning Incremental Learning
College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China · Shenzhen University of Advanced Technology, Shenzhen, China · Harbin Institute of Technology, Shenzhen, China +1
NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning
Aug 5, 2026Seyed Roozbeh Razavi Rohani, Khashayar Khajavi, Wesley Chung +2Class-Incremental Learning Dynamic Neural Networks
Department of Computer Science Simon Fraser University · Department of Computer Science McGill Univeristy, Mila - Quebec AI Institute
UCBound-Net: Uncertainty-Guided Boundary-Aware Continual Learning for Domain-Incremental Ultrasound Segmentation
Aug 2, 2026Mohammad Amanour RahmanUltrasound Image Segmentation Continual Learning
Ahsanullah University of Science and Technology, Dhaka, Bangladesh
Relative Parameter Importance in Task-Agnostic Replay-Free Continual Learning
Aug 1, 2026Malavika Suresh, Ikechukwu Nkisi-Orji, Nirmalie WiratungaClass-Incremental Learning Regularization-Based Continual Learning
Robert Gordon University
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
Jul 29, 2026Ashmith Atmuri, Yashaswini Rao BhogarajulaMemory-Augmented Neural Networks Continual Learning
Arkadhi Research
Continual Video-MLLM Adaptation over Evolving Domains
Jul 21, 2026Rui Cheng, Meixing Shi, Yuxiang Cai +3Video QA Domain-Incremental Learning
School of Software Technology, Zhejiang University, Ningbo, China · Zhejiang Key Laboratory of Digital-Intelligence Service Technology, China · Hong Kong Polytechnic University, Hong Kong SAR +1
The Art of Not Forgetting
Jul 20, 2026Ashmith Atmuri, Akshay Kumar, Yashaswini Rao BhogarajulaMemory-Augmented Neural Networks Domain-Incremental Learning
Arkadhi Labs
Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection
Jul 19, 2026Aoting Zhang, Dongbao Yang, Chang Liu +3Domain-Incremental Learning
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences · Nankai University +2
Domain-Incremental Remote Sensing Change Detection via Difference-Guided Adaptation and Frequency-Decoupled Distillation
Jul 14, 2026Daifeng Peng, Yaning Li, Haiyan GuanRemote Sensing Change Detection Domain-Incremental Learning
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
When Does Continual Learning Require Learning
Jul 8, 2026Anne Harrington, Nayan Saxena, Michael Murphy +7Continual Learning for LLMs Concept Drift
1UC Berkeley · 2Independent · 3Capital Fund Management
FlatManifold: Robust Continual Learning under Severe Label Noise and Domain Shifts via Intrinsic Manifold Flattening
Jul 6, 2026Rai Hisada, Kanji TanakaContinual Learning Domain-Incremental Learning
Department of Mechanical Engineering, Faculty of Engineering, University of Fukui, Fukui 910-8507, Japan
ContiStain: Cross-Domain Relation-Preserving Distillation for Continual Multi-Domain Virtual IHC Staining
Jul 4, 2026Fuqiang Chen, Yifeng Wang, Hongpeng Wang +1Computational Pathology Virtual Staining
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China · School of Computer Science and Technology, Tsinghua University, Beijing, China
Dual-Selective Network for Domain-Incremental Change Detection
Jul 2, 2026Yuzhi He, Junxi Huang, Haorui Wu +1Remote Sensing Change Detection Domain-Incremental Learning
Xidian University, Xi’an, China
Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
Jun 29, 2026Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer +3VLM Adaptation Domain-Incremental Learning
School of Computer Science and IT, Adelaide University, Australia. · Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia · School of Artificial Intelligence and Computer Science, Nantong University, China +1
Fast and Slow Variational Continual Learning
Jun 22, 2026Subarnaduti Paul, Yohan Jung, Mohammad Emtiyaz Khan +3Continual Learning for LLMs Deep Learning Optimization
Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany · Department of Computer Science & AI, Jeonbuk National University, Republic of Korea · Department of Computer Science, TU Darmstadt, Darmstadt, Germany +3

Oct 28, 2026J/K move · Enter open · S save

Episodic looking - using reinforcement learning to bias perception enhances few-shot and continual learning with protection from forgetting

David Rawlinson, Gideon Kowadlo

Cerenaut Melbourne Australia

Continual Learning Catastrophic Forgetting Domain-Incremental Learning Few-Shot Learning Active Perception

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There is a fundamental contradiction between the desire for models that can generalize, and models that can continuously adapt to recent input with changing statistics. These conditions, captured in few-shot and continual learning settings, can bias or disrupt learned representations. This harms generalization to a broader context, leaving the model unable to overcome input variance. This paper proposes a solution based on the Complementary Learning System architecture (CLS), in which a Short-Term Memory (STM) delegates generalization to a slow, statistical Long-Term Memory (LTM). Delegation is achieved by working in a space of robust, generalized perceptions produced by the LTM. With this separation, the STM can focus on memorizing recent input and avoiding interference between memories. We show that on an image classification task, the resulting CLS model is more resistant to forgetting in a domain-incremental continual learning setting than a baseline ResNet LTM, and generalizes more effectively in a few-shot setting than other continual learners. In the continual setting, overall accuracy is slightly worse than existing continual learning models, but with a different mix of performance characteristics. Results analysis suggests that the model is limited by the representations and representational plasticity of the LTM. In future work, we will seek to address this by replaying STM memories to the LTM.