Incremental Learning

Follow this topic Ask about this topic

Momentum

13 papers in the last four weeks, up 63% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 103

All topics

Multi-Scale Structural Features for Continual, Comprehensible Visual Recognition in a Developmental Learning Framework
Jul 28, 2026Zeki Doruk ErdenClass-Incremental Learning Incremental Learning
Sabanci University, Istanbul, Turkey
Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning
Jul 26, 2026Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli +2Continual Learning for LLMs Incremental Learning
College of Health Solutions, Arizona State University, Phoenix, Arizona, USA · Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA +1
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
Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning
Jul 24, 2026Haichen Zhou, Yazhe Lyu, Yixiong Zou +2Class-Incremental Learning Incremental Learning
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
The Art of Not Forgetting
Jul 20, 2026Ashmith Atmuri, Akshay Kumar, Yashaswini Rao BhogarajulaMemory-Augmented Neural Networks Domain-Incremental Learning
Arkadhi Labs
Miles: Metric Learning with Expandable Subspace for Pre-Trained Model-Based Class-Incremental Learning
Jul 20, 2026Kai Jiang, Zisong Lin, Hongyuan Zhang +2Class-Incremental Learning Metric Learning
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China · The University of Hong Kong, Hong Kong SAR, China · Institute of Artificial Intelligence (TeleAI) of China Telecom, China
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
Can a Language Model Learn Facts Continually in Its Weights?
Jul 13, 2026Charles O'Neill, Max Kirkby, Jonathon Liu +1Knowledge Editing Continual Learning for LLMs
Baseten
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
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Jun 27, 2026Wenhao Yuan, Chenchen Lin, Jian Chen +3Class-Incremental Learning Federated Continual Learning
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China · School of Artificial Intelligence, Sun Yat-sen University, China.
When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning
Jun 26, 2026Tanguy Dieudonné, Giulia Lanzillotta, Enis Simsar +2Continual Learning Low-Rank Adaptation
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning
Jun 25, 2026Augustinas Jučas, Yangchen PanClass-Incremental Learning Continual Learning
Department of Computer Science University of Oxford · Department of Engineering Sciences University of Oxford
Geometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning
Jun 24, 2026Hongye Xu, Bartosz KrawczykClass-Incremental Learning Incremental Learning
Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
Incremental Learning in Mirror Flows
Jun 22, 2026Raphaël Berthier, Loucas Pillaud-VivienContinual Learning Mirror Descent
Sorbonne Université, Inria, Centre Inria de Sorbonne Université, Paris, France · CERMICS, CNRS, ENPC, Institut Polytechnique de Paris, Marne-la-Vallée, France
Black-Box Continual Learning for Vision-Language Models
Jun 22, 2026Yuting Li, Weihang Fang, Haoyuan Gao +4Incremental Learning Continual Learning for VLMs
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), Beijing, China +2
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
Towards Whole Hand and Wrist Kinematic Tracking with a Wearable A-Mode Ultrasound Probe
Jun 21, 2026Giusy Spacone, Luca Benini, Andrea CossettiniWearable Sensing Incremental Learning
Integrated Systems Laboratory, ETH Zurich, Zürich, Switzerland · DEI, University of Bologna, Bologna, Italy
Continual Self-Improvement with Lightweight Experiential Latent Memories
Jun 16, 2026Vaggelis Dorovatas, Nancy Kalaj, Rahaf AljundiContinual Learning for LLMs Memory-Augmented Language Models
Toyota Motor Europe · University of Trento
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
Jun 14, 2026Thinh T. H. Nguyen, Khoa D. Doan, Binh T. Nguyen +2Class-Incremental Learning Federated Continual Learning
VinUniversity, Hanoi, Vietnam · VNU-HCM, University of Science, Ho Chi Minh City, Vietnam · Technische Universität Berlin, Germany
Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection
Jun 13, 2026Aoting Zhang, Dongbao Yang, Chang Liu +2Class-Incremental Learning Detection Transformer
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences · Nankai University +2
ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
Jun 10, 2026Jiangtao Kong, Peijun Zhao, Chun-Fu Chen +4Large Vision-Language Models Incremental Learning
Department of Computer Science, William & Mary, Virginia, USA · Global Technology Applied Research, JPMorgan-Chase, USA · Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi
Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio
Jun 9, 2026Avi Gupta, Nilotpal Sinha, Vishnu Raj +4Class-Incremental Learning Audio-Visual Representation Learning
IIIT Delhi · Dolby Laboratories · IIT Kanpur +1
HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers
Jun 8, 2026Daniel Vila-Cruz, Laura Morán-Fernández, Verónica Bolón-CanedoClass-Incremental Learning Energy-Efficient ML
Universidade da Coruña CITIC A Coruña, Spain
Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning
Jun 4, 2026Hongye Xu, Bartosz KrawczykClass-Incremental Learning Prototype-Based Classification
Chester F. Carlson Center for Imaging Science · Rochester Institute of Technology
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
Jun 3, 2026Fan Zhang, Sijin Zheng, Fei Ma +4Class-Incremental Learning Remote Sensing
School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China · School of Information Science and Technology and the Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, China · Beihang University, Beijing 100191, China +1
Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
Jun 2, 2026Zhonggai Wang, Kai Fang, Guangyu GaoClass-Incremental Learning Weakly Supervised Image Segmentation
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification
Jun 2, 2026Pablo García-Santaclara, Bruno Fernández-Castro, Rebeca Pilar Díaz-RedondoClass-Incremental Learning Multivariate Time Series Classification
atlanTTic – ICLAB, Universidade de Vigo, Vigo 36310, Spain · Centro Tecnolóxico de Telecomunicacións de Galicia (GRADIANT), Vigo 36214, Spain
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
Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
May 28, 2026Kellian Cottart, Théo Ballet, Djohan Bonnet +1Bayesian Neural Networks Binary Neural Networks
Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France · Forschungszentrum Jülich, Germany

Jul 28, 2026, cs.LGJ/K move · Enter open · S save

Multi-Scale Structural Features for Continual, Comprehensible Visual Recognition in a Developmental Learning Framework

Zeki Doruk Erden

Sabanci University, Istanbul, Turkey

Class-Incremental Learning Incremental Learning Topological Representation Learning Image Classification

Open paper AskPDF arXiv

Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure. A recently proposed developmental, gradient-free learning framework addresses these limitations by learning a discrete, topological model of its inputs through local variation and selection, yielding an inherent continual-learning guarantee: new observations refine existing structure without overwriting past knowledge, and without replay buffers or predefined task boundaries. Its extension to visual inputs demonstrated this principle on shape recognition, but relied on a feature representation of limited expressivity that capped recognition accuracy. We introduce a new visual feature representation that encodes shape structure across multiple scales, capturing edge and contour features together with their spatial relations, and integrate it with the network-refinement learning process; we further improve the learning dynamics and the read-out used to predict from the learned model. The study targets two-dimensional shape, with class-incremental MNIST as a controlled, interpretable benchmark in which continual-learning behavior can be measured directly. Our approach substantially increases accuracy over the prior representation, matching or exceeding replay- and regularisation-based baselines at comparable storage while storing no past data, and preserves the framework's defining behavior: earlier-learned classes are retained as new ones are introduced, with no destructive adaptation, and the learned representations remain human-interpretable. What separates the methods is retention: the baselines surrender most of a just-trained class within its own cycle and relearn it afterwards, which ours does not. The significance lies in the manner of learning. The system integrates information one sample at a time while provably preserving its responses to...

Momentum

Related topics

Latest papers 103