Online Continual Learning

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12 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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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
Theory of Continual Learning Against Data Poisoning Attacks
Jun 29, 2026Yiting Hu, Lingjie DuanContinual Learning Adversarial Robustness
Singapore University of Technology and Design, Singapore · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.
Gradient-Free Warm-Start Library Recovery: an Amortized-Regret Separation
Jun 19, 2026Jianwei LouContinual Learning Online Continual Learning
RailMind Systems, Neuss, Germany
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
LargeMonitor: Monitoring Online Task-Free Continual Learning via Large Pretrained Models
Jun 8, 2026Mingqi Yuan, Xiaoquan Sun, Shihao Luo +1Distribution Shift Concept Drift
Agentic Intelligence Lab | HKU · Qicore Tech
Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
Jun 4, 2026Parth Asawa, Christopher M. Glaze, Gabriel Orlanski +7Continual Learning for LLM Agents Continual Learning for LLMs
UC Berkeley · Snorkel AI · University of Wisconsin-Madison +1
CLaaS: Continual learning as a service for sample efficient online learning
Jun 4, 2026Kion Fallah, Silen Naihin, Barak Widawsky +1Continual Learning for LLM Agents Continual Learning
1Resolute Labs · 2South Park Commons · 3Incept Labs
SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition
May 30, 2026Jayanta Dey, Shikhar Srivastava, Itamar Lerner +2Memory-Augmented Neural Networks Continual Learning
University of Texas at San Antonio, USA · University of Rochester, USA
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
MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
May 18, 2026Ankita Awasthi, Marco Apolinario, Kaushik RoyClass-Incremental Learning Meta-Learning
Purdue University, USA · TU Delft, Netherlands
Online Continual Learning with Dynamic Label Hierarchies
May 12, 2026Xinrui Wang, Shao-Yuan Li, Bartłomiej Twardowski +2Class-Incremental Learning Hierarchical Classification
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics · MIIT Key Laboratory of Pattern Analysis and Machine Intelligence · Computer Vision Center, Spain +4
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
May 12, 2026Phu-Hoa Pham, Chi-Nguyen Tran, Nguyen Lam Phu Quy +3Class-Incremental Learning Continual Learning
Faculty of Information and Technology University of Science Vietnam National University Ho Chi Minh City, Vietnam · Department of Computer Science University of Warwick Coventry, United Kingdom
Learning to Forget: Continual Learning with Adaptive Weight Decay
Apr 29, 2026Aditya A. Ramesh, Alex Lewandowski, Jürgen SchmidhuberDeep Learning Optimization Weight Decay
The Swiss AI Lab, IDSIA USI-SUPSI, Lugano, Switzerland · University of Alberta, Alberta Machine Intelligence Institute, Edmonton, Canada · Center of Excellence for Generative AI, KAUST, Thuwal, Saudi Arabia
Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning
Nov 11, 2025Raphaël Bayle, Martial Mermillod, Robert M. FrenchContinual Learning Coreset Selection
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France · LEAD-CNRS UMR 5022, U. de Bourgogne Europe, Dijon, France
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Jun 12, 2025Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2Continual Learning for LLMs Low-Rank Adaptation
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · RIKEN AIP, Tokyo, Japan
AIR: Analytic Imbalance Rectifier for Continual Learning
Aug 19, 2024Di Fang, Yinan Zhu, Zhiping Lin +3Class-Imbalanced Learning Class-Incremental Learning
School of Future Technology, South China University of Technology, Guangzhou, China · Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China

Jun 30, 2026, cs.CVJ/K move · Enter open · S save

CLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning

Julien Lefebvre, Stefan Duffner, Mathieu Lefort

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

Self-Supervised Learning Replay-Based Continual Learning Online Continual Learning Incremental Learning Self-Supervised Visual Representation Learning

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Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints. Existing methods rely either on replay buffers that exploit latent space structure, or on regularization alone. We present CLIMB (Continual Learning with Intelligent Memory Bank), which combines both simultaneously. Our method introduces a hierarchical centroid-based memory, bounded in total number of stored images, combined with knowledge distillation on replayed examples to limit representation drift. The memory groups similar images into centroids, providing hard-to-discriminate examples for contrastive learning while covering the diversity of observed distributions. Experiments on Split CIFAR-100 and Split ImageNet-100, on standard benchmarks from the state-of-the-art as well as a new protocol with irregular task distributions show that CLIMB outperforms state-of-the-art OCSSL methods.

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