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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Continual Learning without Continual Training
Oct 7, 2026Nikita Narayanan, Ritham Majumdarr, Sonali ParbhooContinual Learning Class-Incremental Learning
Imperial College London
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
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
Domain-Incremental Learning for Multi-Channel Replay Speech Detection
Sep 11, 2026Michael NeriSpeech Processing Domain-Incremental Learning
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
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 Domain-Incremental 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
Domain-incremental audio classification using domain-specific experts and prototype classifier
Jun 22, 2026Jongyeon Park, Do-Hyeon Lim, Sang-won Park +4Generative Replay Domain-Incremental Learning
Dept. of AI Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea · Dept. of EECS, Gwangju Institute of Science and Technology, Gwangju 61005, Korea · AI Lab., R&D Center, Hanwha Vision, Seongnam-si, Gyeonggi-do 13488, Korea
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
Jun 12, 2026Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar +1Domain-Incremental Learning Cross-Domain Transfer Learning
San Diego State University, San Diego, CA, USA · University of Utah, Salt Lake City, UT, USA · University of Maine, Orono, ME, USA
Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams
May 29, 2026Jonathan Swinnen, Tinne TuytelaarsDomain-Incremental Learning Test-Time Training
ESAT, KU Leuven, Leuven, Belgium
Coarse-to-Fine Domain Incremental Learning with Attentive Distillation for Mining Footprint Segmentation in Multispectral Imagery
May 23, 2026Alif Tri Handoyo, Vincent C. S. Lee, Rizka Widyarini Purwanto +3Image Segmentation Remote Sensing Image Segmentation
Monash University, Indonesia · Monash University, Australia · Northeastern University, China +1
On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis
May 19, 2026Danu KimMedical Diagnosis Continual Learning
Korea International School, Jeju Campus, Jeju-do 63644, Republic of Korea
Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis
May 18, 2026Danu KimChest X-Ray Classification Medical Diagnosis
Korea International School, Jeju Campus, Jeju-do 63644, Republic of Korea
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
May 10, 2026Meng Lou, Hanzhong Guo, Linwei Chen +1Class-Incremental Learning Domain-Incremental Learning
The University of Hong Kong · The Hong Kong University of Science and Technology · Hong Kong Generative AI Research and Development Center

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Continual Learning without Continual Training

Nikita Narayanan, Ritham Majumdarr, Sonali Parbhoo

Imperial College London

Continual Learning Class-Incremental Learning Domain-Incremental Learning Prior-Data Fitted Networks In-Context Learning

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Continual learning requires models to adapt to new domains and new classes while retaining prior knowledge. Many existing methods rely on continued optimization, using regularization, replay, or parameter expansion to prevent new updates from overwriting previously learned knowledge. Instead, we propose replacing continual training with continual inference: a PFN-based model that is meta-trained, and then frozen, adapting to new classes only by extending an in-context evidence set. Our model, Latent Concept PFN, performs in-context Bayesian inference over a latent concept space that captures semantic structure shared across domains and classes. As each new domain or class arrives, exemplars are added to the memory; adaptation reflects updated posterior beliefs over latent concepts rather than gradient updates. No parameters are changed, reducing forgetting. The same method handles both domain and class incremental continual learning without task identity. Concept annotations are only used during meta-training, acting as a soft anchor on the latent space rather than a fixed bottleneck. Unlike fixed-vocabulary concept methods, the model also handles noisy, ambiguous, or incomplete annotations by combining concept labels with raw input evidence to discover distinctions beyond the predefined concept set. Experiments on class and domain incremental learning datasets demonstrate competitive continual learning performance while learning interpretable latent concepts.

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