The proliferation of Few-Shot Class Incremental Learning (FSCIL) methodologies has highlighted the critical challenge of maintaining robust anti-amnesia capabilities in FSCIL learners. In this paper, we present a novel conceptualization of anti-amnesia in terms of mathematical generalization, leveraging the Neural Tangent Kernel (NTK) perspective. Our method focuses on two key aspects: ensuring optimal NTK convergence and minimizing NTK-related generalization loss, which serve as the theoretical foundation for cross-task generalization. To achieve global NTK convergence, we introduce a principled meta-learning mechanism that guides optimization within an expanded network architecture. Concurrently, to reduce the NTK-related generalization loss, we systematically optimize its constituent factors. Specifically, we initiate self-supervised pre-training on the base session to enhance NTK-related generalization potential. These self-supervised weights are then carefully refined through curricular alignment, followed by the application of dual NTK regularization tailored specifically for both convolutional and linear layers. Through the combined effects of these measures, our network acquires robust NTK properties, ensuring optimal convergence and stability of the NTK matrix and minimizing the NTK-related generalization loss, significantly enhancing its theoretical generalization. On popular FSCIL benchmark datasets, our NTK-FSCIL surpasses contemporary state-of-the-art approaches, elevating end-session accuracy by 2.9\% to 9.3\%.
University of South Australia（南澳大学）；Johannes Kepler University（约翰内斯·开普勒大学）；Nantong University（南通大学）；University of Indonesia（印度尼西亚大学）
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem. The assessor is trained in a meta-learning manner with a meta-objective to boost the learning process of the base learner. It performs a soft-weighting mechanism of every sample accepting positive samples while rejecting negative samples. The training objective of a base le…
Guise AI Inc.（Guise AI 公司）；Applied Computational Intelligence Laboratory, Missouri University of Science and Technology（密苏里科技大学应用计算智能实验室）
In streaming data applications incoming samples are processed and discarded, therefore, intelligent decision-making is crucial for the performance of lifelong learning systems. In addition, the order in which samples arrive may heavily affect the performance of online (and offline) incremental learners. The recently introduced incremental cluster validity indices (iCVIs) provide valuable aid in addressing such class of problems. Their primary use-case has been cluster quality monitoring; nonetheless, they have been very recently integrated in a streaming clustering method to assist the cluster…
Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
Dept. of Artificial Intelligence Korea University Seoul, Republic of Korea（人工智能系 韩国大学首尔共和国）；Cognitive Engineering Korea University Seoul, Republic of Korea（认知工程 韩国大学首尔共和国）
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed大学人工智能学院）；University of Trento（特伦托大学）；Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；South China University of Technology（华南理工大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Big Data, Fudan University（复旦大学大数据研究院）；Shanghai Jiao Tong University（上海交通大学）
Mixture of Noise for Pre-Trained Model-Based Class-Incremental Learning
School of Artificial Intelligence, OPtics and ElectroNics, Northwestern Polytechnical University（人工智能学院、光学与电子学院、西北工业大学）；Institute of Artificial Intelligence (TeleAI) of China Telecom（中国电信人工智能研究院）；School of Computer Science & School of Artificial Intelligence, Shanghai Jiao Tong University（计算机科学学院及人工智能学院、上海交通大学）；University of Hong Kong（香港大学）
When learning new tasks in a sequential manner, deep neural networks tend to forget tasks that they previously learned, a phenomenon called catastrophic forgetting. Class incremental learning methods aim to address this problem by keeping a memory of a few exemplars from previously learned tasks, and distilling knowledge from them. However, existing methods struggle to balance the performance across classes since they typically overfit the model to the latest task. In our work, we propose to address these challenges with the introduction of a novel methodology of Tangent Kernel for Incremental…
Towards Robust Incremental Learning under Ambiguous Supervision
School of Software Technology, Zhejiang University（浙江大学软件学院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning
School of Information and Communication Engineering, BUPT（北京邮电大学信息与通信工程学院）；BUPT: Beijing University of Posts and Telecommunications（北京邮电大学）；Engineering Research Center for Information Network, Ministry of Education（教育部信息网络工程研究中心）
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education（教育部可持续城市智能交通工程研究中心）；National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University（西南交通大学智能交通大数据应用技术国家工程实验室）；Sichuan International Travel Healthcare Center, Chengdu Customs（四川国际旅行卫生中心，成都海关）；Endocrinology Department, Hospital of Chengdu University of Traditional Chinese Medicine（成都中医药大学内分泌科）
Class Incremental Learning via Likelihood Ratio Based Task Prediction
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Stanford University（斯坦福大学）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机系）
MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
Gannan University of Science and Technology（赣南科技学院）；Tsinghua University（清华大学）；Queen University（女王大学）；Lingnan University（岭南大学）；Central South University（中南大学）
SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation
University of Surrey, UK（英国萨里大学）；University of Wollongong, Australia（澳大利亚沃拉彭大学）；University of Copenhagen, Denmark（丹麦哥本哈根大学）；University of Exeter, UK（英国埃克塞特大学）；Singapore University of Technology and Design, Singapore（新加坡科技与设计大学）
Stable Routing for Mixture-of-Experts in Class-Incremental Learning
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Pohang University of Science and Technology（浦项科技大学）
标准化随机测度（Normalized Random Measures，NRM）是一类离散随机测度，通常用作贝叶斯非参数模型的先验。Dirichlet过程是NRM的一个众所周知的例子。大多数NRM混合模型的后验推理方法依赖于MCMC方法，因为它们易于实现并且其收敛性已经得到了很好的研究。然而，MCMC经常遭受缓慢的收敛时，接受率低。基于树的推理是另一种确定性后验推理方法，其中贝叶斯层次聚类（BHC）或增量贝叶斯层次聚类（IBHC）已分别为DP或NRM混合（NRMM）模型开发。虽然IBHC是NRMM模型后验推理的一种很有前途的方法，因为它的效率和在线推理的适用性，但它的收敛性不能保证，因为它使用了在多次试验后简单地选择最佳解决方案的算法。本文结合MCMC和IBHC的优点，提出了一种NRMM模型的混合推理算法。IBHC构建的树概述了数据的分区，这指导了Metropolis-Hastings程序采用适当的建议。继承MCMC的性质，我们的树引导MCMC（tgMCMC）是保证收敛，并享受快速收敛由于有效的建议指导树。在合成数据集和真实数据集上的实验证明了我们方法的优点。
