Incremental Learning

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

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Latest papers 103

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Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning
May 28, 2026Xiang Tan, Run He, Yawen Cui +6Class-Incremental Learning Incremental Learning
South China University of Technology · Hong Kong Polytechnic University · Agency for Science, Technology and Research +3
AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
May 27, 2026Zhen-Hao Xie, Yu-Cheng Shi, Da-Wei ZhouClass-Incremental Learning Vision-Language Models
School of Artificial Intelligence, Nanjing University, China · State Key Laboratory of Novel Software Technology, Nanjing University, China
Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias
May 26, 2026James Town, Etienne Boursier, Ben Lewis +2ReLU Neural Networks Neural Network Optimization
University of Warwick · INRIA LMO, Université Paris-Saclay
Dynamic Mixture of Latent Memories for Self-Evolving Agents
May 21, 2026Dianzhi Yu, Vireo Zhang, Hongru Wang +7Continual Learning for LLMs Mixture-of-Experts Language Models
The Chinese University of Hong Kong · Nanyang Technological University · University of Edinburgh +2
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
Stable Routing for Mixture-of-Experts in Class-Incremental Learning
May 17, 2026Zirui Guo, Quan Cheng, Da-Wei Zhou +1Class-Incremental Learning Expert Routing
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China
Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
May 15, 2026Mohammad Ali Vahedifar, Abhisek Ray, Qi ZhangClass-Incremental Learning Continual Learning
DIGIT and Department of Electrical and Computer Engineering, Aarhus University, Denmark
Understanding Imbalanced Forgetting in Rehearsal-Based Class-Incremental Learning
May 14, 2026Alberto Tamajo, Srinandan Dasmahapatra, Rahman AttarClass-Incremental Learning Gradient Interference
School of Electronics and Computer Science, University of Southampton, SO17 1BJ Southampton, United Kingdom
Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
May 13, 2026Hao Sun, Zi-Jun Ding, Da-Wei ZhouClass-Incremental Learning VLM Adaptation
School of Artificial Intelligence, Nanjing University · State Key Laboratory for Novel Software Technology, Nanjing University
Low-Rank Adapters Initialization via Gradient Surgery for Continual Learning
May 12, 2026Joana Pasquali, Ramiro N. Barros, Arthur S. Bianchessi +7Continual Learning for LLMs Gradient Interference
MALTA, Machine Learning Theory and Applications Lab, PUCRS, Porto Alegre, Brazil · Kunumi Institute, Brazil
KAN-CL: Per-Knot Importance Regularization for Continual Learning with Kolmogorov-Arnold Networks
May 12, 2026Minjong CheonContinual Learning Kolmogorov-Arnold Networks
Sejong University Department of Computer Science and Engineering
Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning
May 12, 2026Huiyu Yi, Zhiming Xu, Dunwei Tu +3Class-Incremental Learning Prototype-Based Classification
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · School of Computer Science, Nanjing University, Nanjing, China
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
Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
May 9, 2026Zhen-Hao Xie, Yan Wang, Hao Sun +3Class-Incremental Learning Continual Learning
School of Artificial Intelligence and the State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China
Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Models
May 8, 2026Mengxin Qin, Xiang Zhang, Kun Wei +2Class-Incremental Learning Vision-Language Models
School of Electronic Engineering, Xidian University Xi’an 710071, China
DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models
May 8, 2026Mengxin Qin, Xiang Zhang, Xi Wang +3Vision-Language Models Fine-Tuning
School of Electronic Engineering, Xidian University Xi’an 710071, China
SR $^2$ -LoRA: Self-Rectifying Inter-layer Relations in Low-Rank Adaptation for Class-Incremental Learning
May 8, 2026Fengqiang Wan, Yipeng Lin, Kan Lv +1Class-Incremental Learning Low-Rank Adaptation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Self-Consolidating Language Models: Continual Knowledge Incorporation from Context
May 8, 2026Zekun Wang, Anant Gupta, Zihan Dong +1Continual Learning for LLMs Long-Context Language Modeling
Georgia Institute of Technology
HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
May 7, 2026Yu Feng, Zhen Tian, Haoran Luo +8Prompt Learning Domain-Incremental Learning
China Mobile Research Institute, China · Beijing University of Posts and Telecommunications, China · Nanyang Technological University, Singapore +1
CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning
May 7, 2026Md Anwar Hossen, Fatema Siddika, Juan Pablo Munoz +2Continual Learning for LLMs Low-Rank Adaptation
1Iowa State University · 2Maro Systems, USA · University of California, Berkeley +1
Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping
May 5, 2026Kaustubh Pethkar, Ziyang Xiong, Zuofeng Shang +1Continual Learning for LLMs Memorization in Language Models
New Jersey Institute of Technology · University of California, Berkeley
Towards Open World Sound Event Detection
May 5, 2026P. H. Hai, L. T. Minh, L. H. SonContinual Learning Detection Transformer
VNU University of Engineering and Technology, Hanoi, 100000, Vietnam · Artificial Intelligence Research Center, VNU Information Technology Institute, Hanoi, 100000, Vietnam
MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
May 5, 2026Shishir Muralidhara, Didier Stricker, René SchusterDomain-Incremental Learning Incremental Learning
German Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany · University of Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Str. 47, 67663 Kaiserslautern, Germany
Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning
May 5, 2026Taigo Sakai, Kazuhiro HottaClass-Incremental Learning Long-Tail Learning
Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks
Apr 27, 2026Kevin McKee, Thomas Hazy, Yicong Zheng +2Continual Learning Incremental Learning
Astera Institute
Incremental learning for audio classification with Hebbian Deep Neural Networks
Apr 20, 2026Riccardo Casciotti, Francesco De Santis, Alberto Antonietti +1Hebbian Learning Continual Learning
Signal Processing Research Centre, Tampere University, Tampere, Finland · Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Italy
Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting
Apr 20, 2026Hyeonseo Jang, Hyuk Kwon, Kibok LeeClass-Incremental Learning Vision-Language Models
Yonsei University
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Apr 16, 2026Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1Class-Incremental Learning Concept Bottleneck Models
Department of Electrical and Computer Engineering, University of California San Diego · Department of Computer Science and Engineering, University of California San Diego · Halıcıoğlu Data Science Institute, University of California San Diego
Mistake gating leads to energy and memory efficient continual learning
Apr 15, 2026Aaron Pache, Mark CW van RossumEnergy-Efficient ML Continual Learning
School of Mathematical Sciences University of Nottingham, Nottingham NG7 2RD, United Kingdom · School of Psychology University of Nottingham, Nottingham NG7 2RD, United Kingdom

May 28, 2026, cs.CVJ/K move · Enter open · S save

Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning

Xiang Tan, Run He, Yawen Cui, Mengchen Zhao+5

South China University of Technology · Hong Kong Polytechnic University · Agency for Science, Technology and Research+3

Class-Incremental Learning Incremental Learning Catastrophic Forgetting

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Class-Incremental Learning (CIL) with pre-trained models (PTMs) aims to sequentially adapt PTMs to new categories without forgetting old knowledge. Built upon PTMs, existing adapter-based methods mainly train models via distinct task-specific adapters, and present a uniform knowledge allocation for each adapter during inference. However, this allocation mechanism ignores the nature of task discrepancy and leads to suboptimal utilization of adapters. Also, under CIL constraint, an allocator is prone to forgetting when tasks evolve. To address these issues, we propose a Non-Forgetting Allocation with Bi-Level Competition (NoFA-BC). NoFA-BC constructs a non-forgetting allocator (NFA) by transforming the allocator training into a recursive least-squares problem and achieves an allocator equivalent to that trained with all data. Based on the NFA, a Bi-Level Competition (BLC) including an intra-task level Winner-Takes-All (WTA) mechanism and inter-task Last-Ones-Fall (LOF) elimination is proposed to provide better allocation of adapter knowledge. WTA extracts the most significant logit within a task to represent the adapter's contribution and LOF suppresses the irrelevant adapters. With BLC, participation ratio of each adapter can be tailored for each input. Moreover, a Stability Enhancement (SE) process is incorporated to further improve the performance of old tasks.

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Latest papers 103