Replay-Based Continual Learning

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9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

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Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance
Oct 6, 2026Khac Duc Giang Nguyen, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor AlsahagReplay-Based Continual Learning Object Detection
University of Amsterdam Amsterdam, The Netherlands · Department of Computer Science and Technology, SUNY Empire State University Saratoga Springs, NY, USA
What Must Replay Preserve? Separating Correctable Bias from Class Correspondence
Oct 5, 2026BoRen Deng, Xiangyue Ma, Chenglong Li +1Class-Incremental Learning Replay-Based Continual Learning
Beijing University of Posts and Telecommunications · Beihang University · Beijing University of Technology
Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining
Sep 30, 2026Lukas Thede, Shengzhuang Chen, Stefan Winzeck +3Continual Learning for LLMs Replay-Based Continual Learning
University of Tübingen, Tübingen AI Center · Helmholtz Munich · Munich Center for Machine Learning (MCML) +3
Experience-Driven Continual Learning of Terrain Traversability for Quadruped Robots
Sep 30, 2026Luca Bricarello, João Carlos Virgolino Soares, Alberto Sanchez-Delgado +2Continual Robot Learning Safe Robot Navigation
Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia, Genoa, Italy. · Universit`a degli Studi di Genova, Genoa, Italy.
ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning
Sep 30, 2026Sungmin Kang, Zhengzhong Tu, Sunwoo LeeFederated Continual Learning Replay-Based Continual Learning
Texas A&M University · Inha University
HiTS-CL: A Continual Learning Framework for Long-Horizon Temporal Knowledge Graph Extrapolation
Sep 29, 2026Yansong Liu, Rui Liu, Yuan Zuo +6Temporal Link Prediction Replay-Based Continual Learning
Beihang University Beijing, China · Beijing University of Posts and Telecommunications Beijing, China · Hubei Engineering University Xiaogan, Hubei, China
Reliable Replay through Spatial Coherence in Online Continual Learning
Sep 27, 2026Haixiang Sun, Jiefu Zhang, Yinghao He +4Continual Learning for LLMs Experience Replay
Purdue University West Lafayette, IN 47907, USA
Beyond Class Marginals: Bounding Rehearsal Gaps without Freezing Class Co-occurrence
Sep 22, 2026Congren Dai, Nat Roongjirarat, Fei YeClass-Incremental Learning Replay-Based Continual Learning
Imperial College London · King’s College London · University of Electronic Science and Technology of China
Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning
Sep 16, 2026Everest Yang, Skye Thompson, George D. KonidarisNon-Stationary RL Continual Robot Learning
Brown University, Providence, RI, USA
Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation
Sep 15, 2026Hojin Lee, Yunho Lee, Daniel A Duecker +1Continual Robot Learning Robot Navigation
Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Sep 12, 2026Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1Generative Replay Deep Q-Learning
Delft University of Technology, Delft, The Netherlands. · Quantum Computing Division, QuTech, Delft, The Netherlands. · Indian Institute of Technology (BHU), Varanasi, India. +1
Beyond Endpoint Scores: Time- and Capacity-Conditioned Evaluation of Continual Knowledge Updating
Sep 3, 2026Heejin ChoiContinual Learning for LLMs Continual Learning
Yonsei University · Sooil Development · NCreate
Beyond Forgetting: Diagnosing and Harnessing Shared Reasoning in Continual RLVR
Aug 19, 2026Lirui Luo, Guoxi Zhang, Hongming Xu +3RL for Language Model Reasoning Replay-Based Continual Learning
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · State Key Laboratory of General Artificial Intelligence, BIGAI · Beijing Institute of Technology +1
Uniform Herding: Exemplar Replay with Representation Refresh
Aug 13, 2026Krishna SubediClass-Incremental Learning Replay-Based Continual Learning
Neryva
Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning
Aug 12, 2026Tieliang Gong, Zhongbo Zhang, Wen Wen +1Information-Theoretic Generalization Bounds Replay-Based Continual Learning
Tieliang Gong, Zhongbo Zhang and Wen Wen are with the School of Computer Science and Technology, Xi’an Jiaotong University, China · Yong-Jin Liu is with Department of Computer Science and Technology, Tsinghua University, China
NeuroGuard: Neural Gradient Update Aware of Representation Damage
Aug 8, 2026Taigo Sakai, Kazuhito HottaClass-Incremental Learning Adaptive Loss Weighting
Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
STAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models
Aug 6, 2026Songpan Gao, Yajie Zhang, Guanxing Chen +9Continual Learning Medical Image Classification
Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations
Aug 5, 2026Xujia Chen, Xinyue Hu, Letian Chen +2PDE Surrogate Modeling Replay-Based Continual Learning
Department of Automation, Tsinghua University, Beijing, 100084, China
Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning
Jul 25, 2026Tao Zhang, Qixuan Fan, Yiyuan Liang +7Class-Incremental Learning Synthetic-to-Real Domain Adaptation
Huazhong University of Science and Technology, Wuhan, China · State Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, China · University of Science and Technology of China, Anhui, China +1
MA-DAR: Manifold-Aligned Dynamic Adaptive Routing for Continual Temporal Knowledge Graph Reasoning
Jul 24, 2026Xiangjun Shi, Chong Mu, Jinchuan Zhang +3Replay-Based Continual Learning Temporal KGs
School of Computer Science and Engineering, University of Electronic Science and Technology of China
The World Model Remembers, the Actor Forgets: Dream Rehearsal for Continual Model-Based RL
Jul 22, 2026Gurp NijjerGenerative Replay Reinforcement Learning
Quantegra Research, Canada
InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection
Jul 18, 2026Jikang Cheng, Hao Shen, Xueyi Zhang +4Deepfake Detection Replay-Based Continual Learning
2Peking University · 1Huazhong Agricultural University · 3National University of Singapore +2
Rethinking Transfer in Continual Learning: A Replay-Based Realisation
Jul 17, 2026Yang Meng, Zhenya Liu, Zhuokai Zhao +1Continual Learning for LLMs Experience Replay
Department of Computer Science, University of Chicago
Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection
Jul 16, 2026Peisheng Qian, Jie Xu, Xulei Yang +1Class-Incremental Learning 3D Object Detection
Singapore University of Technology and Design · Institute for Infocomm Research, A*STAR, Singapore
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jul 13, 2026Jing Liu, Chenxuanyin Zou, Jiayang Ren +5Multimodal Federated Learning Generative Replay
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · College of Future Information Technology, Fudan University, Shanghai 200433, China · Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada +3
CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment
Jul 13, 2026Tingxu Yan Ye YuanReplay-Based Continual Learning Catastrophic Forgetting
1Tingxu Yan and Ye Yuan* are with the College of Computer and Information Science, Southwest University, Chongqing 400715, China
Hybrid Continual Learning for Low-Resource Australian Aboriginal Language Identification
Jul 11, 2026Pravina Mylvaganam, Ting Dang, Eliathamby Ambikairajah +2Continual Learning Speech Processing
University of New South Wales, Australia · University of Melbourne, Australia · Massachusetts Institute of Technology, USA
CLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning
Jun 30, 2026Julien Lefebvre, Stefan Duffner, Mathieu LefortSelf-Supervised Learning Replay-Based Continual Learning
Universite Claude Bernard Lyon 1, CNRS, INSA Lyon, LIRIS, UMR 5205, 69622 Villeurbanne, France · INSA Lyon, CNRS, Universite Claude Bernard Lyon 1, LIRIS, UMR 5205, 69621 Villeurbanne, France · Universite Claude Bernard Lyon 1 / Univ. Rennes, Inria, CNRS, LIRIS UMR 5205 / IRISA UMR 6074, France
Prototype Latent World Model Replay for Class-Incremental Learning
Jun 28, 2026Weizhi Nie, Hui Wang, Weijie Wang +1Class-Incremental Learning Replay-Based Continual Learning
Tianjin University
World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
Jun 25, 2026Manish Kumar Govind, Dominick Reilly, Smit Patel +2Generative Replay Continual Robot Learning
Department of Computer Science University of North Carolina at Charlotte, United States

Oct 6, 2026, cs.CVJ/K move · Enter open · S save

Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance

Khac Duc Giang Nguyen, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag

University of Amsterdam Amsterdam, The Netherlands · Department of Computer Science and Technology, SUNY Empire State University Saratoga Springs, NY, USA

Replay-Based Continual Learning Object Detection Aerial Object Detection Catastrophic Forgetting

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Counter-UAV systems based on thermal infrared detection must stay accurate as operational datasets evolve, yet sequential fine-tuning causes catastrophic forgetting of prior tasks, a problem that remains insufficiently characterized in this domain. This continual-learning study measures the stability-plasticity trade-off in YOLOMG, a YOLOv5-based detector run as a single thermal-infrared stream with the motion channel disabled, trained sequentially across three anti-UAV benchmarks of rising scale difficulty: Anti-UAV-RGBT, Anti-UAV410, and CST Anti-UAV. Naive fine-tuning on CST yields a Forgetting Measure of -0.605 against the Stage 1 ceiling, corresponding to a 90% capability loss, with -0.572 occurring in Stage 3 alone. In contrast, knowledge distillation from a frozen teacher is associated with FM = -0.033 +/- 0.004 across three seeds, corresponding to 95% retention. Because no Stage 2 no-KD control is included, this result establishes retention under KD training rather than a causal KD effect. Per-stratum analysis shows large-target detection collapsing to near zero within the first epoch, despite an inter-stage cosine similarity of 0.987 over the gradient-updated weights, pointing to scale-conditioned gradient imbalance, rather than weight drift, as a candidate mechanism. Scale-Stratified Herding (SSH), a 300-exemplar buffer balanced across four UAV size strata, roughly halves the forgetting (FM = -0.605 to -0.311) and keeps large-target detection non-zero. An ablation attributes the gain primarily to scale stratification rather than herding: random-stratified replay performs at least as well (FM = -0.221 versus -0.311 for SSH). These replay results are single-seed and should therefore be treated as preliminary.

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