South China University of Technology（华南理工大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Institute, Hunan University（湖南大学深圳研究院）；Pazhou Lab（琶洲实验室）
Huiping Zhuang, Yizhu Chen, Di Fang, Run He, Kai Tong, Hongxin Wei, Ziqian Zeng, Cen Chen
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks. The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution. Existing attempts for the GCIL either have poor performance or invade data privacy by saving exemplars. In this paper, we propose a new exemplar-free GCIL technique named generalized analytic continual learning (GACL). The GACL adopts analytic learning (a gradient-free training technique) and delivers an analytical (i.e., closed-form) solution to the GCIL scenario. This solution is derived via decomposing the incoming data into exposed and unexposed classes, thereby attaining a weight-invariant property, a rare yet valuable property supporting an equivalence between incremental learning and its joint training. Such an equivalence is crucial in GCIL settings as data distributions among different tasks no longer pose challenges to adopting our GACL. Theoretically, this equivalence property is validated through matrix analysis tools. Empirically, we conduct extensive experiments where, compared with existing GCIL methods, our GACL exhibits a consistently leading performance across various datasets and GCIL settings. Source code is available at https://github.com/CHEN-YIZHU/GACL.
Targeted for real world scenarios, online continual learning aims to learn new tasks from sequentially available data under the condition that each data is observed only once by the learner. Though recent works have made remarkable achievements by storing part of learned task data as exemplars for knowledge replay, the performance is greatly relied on the size of stored exemplars while the storage consumption is a significant constraint in continual learning. In addition, storing exemplars may not always be feasible for certain applications due to privacy concerns. In this work, we propose a n…
Ca’ Foscari University（威尼斯福斯卡里大学）；University of New South Wales（新南威尔士大学）；Computer Vision Center（计算机视觉中心）
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their abi…
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
School of Software, Shandong University（山东大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
Warsaw University of Technology（华沙理工大学）；IDEAS NCBR；Jagiellonian University（雅盖隆大学）；Gdańsk University of Technology（格但斯克理工大学）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；Tooploox
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Computer Vision Center, Barcelona University of Chinese Academy of Sciences（巴塞罗那大学计算机视觉中心，中国科学院）；State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人学国家重点实验室）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造研究所）
Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）
Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, recommendation requires continual adaptation to take into account new and obsolete items and users, and requires "continual learning" in real-life applications. In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user …
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）
Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；University of Chinese Academy of Sciences（中国科学院大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）
Continual learning in online scenario aims to learn a sequence of new tasks from data stream using each data only once for training, which is more realistic than in offline mode assuming data from new task are all available. However, this problem is still under-explored for the challenging class-incremental setting in which the model classifies all classes seen so far during inference. Particularly, performance struggles with increased number of tasks or additional classes to learn for each task. In addition, most existing methods require storing original data as exemplars for knowledge replay…
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Center for Environmental Intelligence, VinUniversity（VinUniversity环境智能中心）；Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）；Faculty of Information Systems, Phenikaa University（Phenikaa大学信息系统学院）
LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism, Ministry of Culture and Tourism, Sun Yat-sen University（文化和旅游部可持续旅游智能评估技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部机器智能与先进计算重点实验室）
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）
ICICLE: Interpretable Class Incremental Continual Learning
Jagiellonian University（雅盖隆大学）；Ardigen SA（阿迪根公司）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；IDEAS NCBR（IDEAS国家计算智能研究中心）
Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）
Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) b…
Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applications where new data come in a stream, we study NLG in a "continual learning" setting to expand its knowledge to new domains or functionalities incrementally. The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before. To this end, we propose a …
Food image classification is challenging for real-world applications since existing methods require static datasets for training and are not capable of learning from sequentially available new food images. Online continual learning aims to learn new classes from data stream by using each new data only once without forgetting the previously learned knowledge. However, none of the existing works target food image analysis, which is more difficult to learn incrementally due to its high intra-class variation with the unbalanced and unpredictable characteristics of future food class distribution. I…
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
Technical University of Munich（慕尼黑技术大学）；TUM School of Computation, Information and Technology（TUM计算、信息与技术学院）；Department of Computer Engineering, Learning Systems and Robotics Lab（计算机工程系、学习系统与机器人实验室）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；Robotics Institute Germany（德国机器人研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
The University of Melbourne（墨尔本大学）；Fortemedia Singapore（Fortemedia新加坡）；Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）
Continual Learning in Open-vocabulary Classification with Complementary Memory Systems
Siebel School of Computing and Data Science（西贝尔计算与数据科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California Merced（加州大学默塞德分校）
Xi’an Jiaotong University（西安交通大学）；Singapore Management University（新加坡管理大学）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）
State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can…
Exemplar-free incremental learning is extremely challenging due to inaccessibility of data from old tasks. In this paper, we attempt to exploit the knowledge encoded in a previously trained classification model to handle the catastrophic forgetting problem in continual learning. Specifically, we introduce a so-called knowledge delegator, which is capable of transferring knowledge from the trained model to a randomly re-initialized new model by generating informative samples. Given the previous model only, the delegator is effectively learned using a self-distillation mechanism in a data-free m…
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）
Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene properties. In this paper, we make a further step towards continual learning of the implicit scene representation directly from sequential observations, namely Continual Neural Mapping. The proposed problem setting bridges the gap between batch-trained implicit neural representations and commonly used streaming data in robotics and vision communities. We introduce an experience replay approach to tackle an exemplary task of c…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；University of Moratuwa（莫拉图瓦大学）；Toyota Motor Europe（丰田汽车欧洲公司）
With the success of pretraining techniques in representation learning, a number of continual learning methods based on pretrained models have been proposed. Some of these methods design continual learning mechanisms on the pre-trained representations and only allow minimum updates or even no updates of the backbone models during the training of continual learning. In this paper, we question whether the complexity of these models is needed to achieve good performance by comparing them to a simple baseline that we designed. We argue that the pretrained feature extractor itself can be strong enou…
Retrospective Feature Estimation for Continual Learning
VinUni-Illinois Smart Health Center（VinUni-伊利诺伊州立健康中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Chinese University of Hong Kong（香港中文大学）；Simular Research（Simular研究公司）；University of Warwick（沃里克大学）
Divide and not forget: Ensemble of selectively trained experts in Continual 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；Jagiellonian University（雅盖隆大学）；Computer Vision Center, Barcelona（巴塞罗那计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）
