Task-Incremental Learning

Latest papers

8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration
Oct 15, 2026Wenju Sun, Qingyong Li, Boyang Li +4Task-Incremental Learning Incremental Learning
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education), Beijing Jiaotong University, Beijing, China, 100044. · College of Computing and Data Science, Nanyang Technological University, Singapore, 639798. · Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Fujian, China, 354300.
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection
Oct 10, 2026Łukasz Marcjan, Dorota Wilk-Kołodziejczyk, Roberto CorizzoTask-Incremental Learning Continual Anomaly Detection
AGH University of Krakow, Krakow, Poland · American University, Washington, DC, USA
AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning
Aug 2, 2026Pengxiang Wang, Hongbo Bo, Jun Hong +2Task-Incremental Learning Stability-Plasticity Dilemma
School of Mathematical Sciences, Peking University, Beijing, China · Population Health Sciences Institute, Newcastle University, Newcastle, UK · School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK +1
Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
May 15, 2026Mohammad Ali Vahedifar, Abhisek Ray, Qi ZhangTask-Incremental Learning Class-Incremental Learning
DIGIT and Department of Electrical and Computer Engineering, Aarhus University, Denmark
DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models
May 8, 2026Mengxin Qin, Xiang Zhang, Xi Wang +3Task-Incremental Learning Vision-Language Models
School of Electronic Engineering, Xidian University Xi’an 710071, China
Same Methods, Different Rankings: Trainable Depth as an Evaluation Variable in Continual Learning
Apr 23, 2026Paul-Tiberiu Iordache, Elena Burceanu, Mihai DascaluTask-Incremental Learning Fine-Tuning
Bitdefender, Romania · Politehnica University of Bucharest, Romania
CARL-CXR: Continual Adapter-Based Routing for Task-Unknown Chest Radiograph Classification
Feb 17, 2026Muthu Subash Kavitha, Anas Zafar, Amgad Muneer +1Task-Incremental Learning Task-Free Continual Learning
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
The Bayesian Approach to Continual Learning: An Overview
Jul 11, 2025Tameem AdelTask-Incremental Learning Class-Incremental Learning
NPL, Maxwell Centre, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 OHE, United Kingdom.

Oct 15, 2026J/K move · Enter open · S save

Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration

Wenju Sun, Qingyong Li, Boyang Li, Wen Wang+3

Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education), Beijing Jiaotong University, Beijing, China, 100044. · College of Computing and Data Science, Nanyang Technological University, Singapore, 639798. · Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Fujian, China, 354300.

Task-Incremental Learning Incremental Learning Class-Incremental Learning Memory-Augmented Neural Networks Memory Consolidation

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Incremental learning (IL) aims to continually acquire new knowledge (plasticity) while retaining previously learned information (stability). However, striking a balance between plasticity and stability remains a significant challenge for intelligent systems. The human brain achieves exceptional balance, owing to various memory units that collaboratively encode and store information. Inspired by human memory mechanisms, this paper introduces an IL framework with knowledge enCoding, Consolidation, and Integration (Co2In). Co2In is designed as a bi-level memory architecture: a working memory for adaptive knowledge acquisition and a long-term memory dedicated to the persistent retention of information. The two memory modules work cooperatively during IL. The working memory first learns from new data to encode knowledge into parameters. Subsequently, Co2In performs a consolidation process to identify underlying patterns in the learned parameters and re-express them into a compact knowledge representation. Next, the knowledge representation and the identified patterns are transformed into separate network layers and integrated into the long-term memory. These designs empower Co2In to accumulate knowledge with high plasticity and stability. We evaluate Co2In on CIFAR-10, CIFAR-100, and Tiny-ImageNet under exemplar-free Class-IL and Task-IL settings. Experimental results show that Co2In achieves state-of-the-art performance with efficient memory consumption.