Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；University of Rochester（罗切斯特大学）
Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, Zheng-Jun Zha
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be achieved under supervision from new classes. To address this problem, we propose a novel self-sustaining representation expansion scheme. Our scheme consists of a structure reorganization strategy that fuses main-branch expansion and side-branch updating to maintain the old features, and a main-branch distillation scheme to transfer the invariant knowledge. Furthermore, a prototype selection mechanism is proposed to enhance the discrimination between the old and new classes by selectively incorporating new samples into the distillation process. Extensive experiments on three benchmarks demonstrate significant incremental performance, outperforming the state-of-the-art methods by a margin of 3%, 3% and 6%, respectively.
Exemplar-Free Class Incremental Learning via Incremental Representation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Mathematics and Statistics, Beijing Technology and Business University（北京工商大学数学与统计学院）
Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data
Sun Yat-sen University（中山大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）；Peng Cheng Laboratory（鹏城实验室）
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning
Yale University（耶鲁大学）；Johns Hopkins University（约翰霍普金斯大学）；University of Freiburg（弗赖堡大学）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
FeTrIL++: Feature Translation for Exemplar-Free Class-Incremental Learning with Hill-Climbing
West University of Timisoara（蒂米什瓦拉西部大学）；Université Paris-Saclay（巴黎-萨克雷大学）；CEA（法国原子能委员会）；LIGM（巴黎路桥学校信息学实验室）；Ecole des Ponts（巴黎路桥学校）；Univ Gustave Eiffel（Gustave Eiffel大学）；CNRS（法国国家科学研究中心）
On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning
The Hong Kong Polytechnic University（香港理工大学）；University College London（伦敦大学学院）；Huazhong University of Science and Technology（华中科技大学）；The University of Queensland（昆士兰大学）
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）
Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond
Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心（BNRist））；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（北京邮电大学智能工程与自动化学院）；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）
Efficient Non-Exemplar Class-Incremental Learning with Retrospective Feature Synthesis
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
Shien-Ming Wu School of Intelligent Engineering（申铭武智能工程学院）；School of Future Technology（未来技术学院）；Department of Electrical and Electronic Engineering（电气与电子工程系）
Non-Exemplar Online Class-incremental Continual Learning via Dual-prototype Self-augment and Refinement
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Huazhong University of Science and Technology（华中科技大学）
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
Communication Center (MICC), University of Florence, Italy Tomaso Trinci. ORCID: 0000-0002-4052-1930 Global Optimization Laboratory, University of Florence, Italy Albin Soutif - Cormerais. ORCID: 0009-0008-1564-2029 LAMP Team, Computer Vision Center, Barcelona, Spain Joost van de Weijer. ORCID: 0000-0002-9656-9706 LAMP Team, Computer Vision Center, Universitat Aut\` o noma de Barcelona, Spain Andrew D. Bagdanov. ORCID: 0000-0001-6408-7043 Media Integration；Communication Center (MICC), University of Florence, Italy Equal contribution
Memory Efficient Class-Incremental Learning for Image Classification
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Shanghai Institute for Advanced Study, Zhejiang University（浙江大学上海高等研究院）
PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning
IDEAS NCBR；Warsaw University of Technology（华沙工业大学）；Gdańsk University of Technology（格但斯克工业大学）；Tooploox；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）
