We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Meanwhile, class-incremental (CI) methods enable learning of new classes in absence of source training data but fail under a domain-shift without labeled supervision. In this work, we effectively identify the limitations of these approaches in the CIDA paradigm. Motivated by theoretical and empirical observations, we propose an effective method, inspired by prototypical networks, that enables classification of target samples into both shared and novel (one-shot) target classes, even under a domain-shift. Our approach yields superior performance as compared to both DA and CI methods in the CIDA paradigm.
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Goethe University Frankfurt（法兰克福歌德大学）
We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. We assume that a labeled source domain and different unlabeled target domains are incrementally observed with the constraint that data corresponding to the current domain is only available at a time. The goal is to preserve the accuracies for all the past domains while generalizing well for the current domain. The IDA setup suffers due to the abrupt differences among the domains and the unavailability of past data including the source domain. Inspired by the notion of generative feature replay, we pr…
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from …
University of Alicante（阿利坎特大学）；University of Edinburgh（爱丁堡大学）
In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is unpredictable and becomes dependent upon the degree of similarity between the distribution of the training set and the distribution of the test set. One of the research topics that investigates this scenario is referred to as domain adaptation. Deep neural networks brought dramatic advances in pattern recognition and that is why there have been many attempts to provide good …
Peking University（北京大学）；South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes, but the classes in the unlabeled target domain increase sequentially. This problem is challenging due to two difficulties. First, source and target label sets are inconsistent at each time step, which makes it difficult to conduct accurate domain alignment. Second, previous target classes are unavailable in the current step, resulting in the forgetting of previous knowledge. To address this problem, we propose a nov…
École de technologie supérieure（高等技术学院）；Genetec Inc.（捷泰科公司）
Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions. While the problem of single-target domain adaptation (STDA) for object detection has recently received much attention, multi-target domain adaptation (MTDA) remains largely unexplored, despite its practical relevance in several real-world applications, such as multi-camera video surveillance. Compared to the STDA problem that may involve large domain shifts between complex source and ta…
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation
Hangzhou Dianzi University（杭州电子科技大学）；Macao Polytechnic University（澳门理工大学）；Beijing Institute of Technology（北京理工大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Khalifa University（哈利法大学）；National University of Sciences and Technology（国家科技大学）；Beihang University（北京航空航天大学）；Mayo Clinic（梅奥诊所）；Abu Dhabi Healthcare Company (SEHA)（阿布扎比医疗公司（SEHA））
Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness. Many researchers have developed autonomous systems to recognize retinopathy via fundus and optical coherence tomography (OCT) imagery. However, most of these frameworks employ conventional transfer learning and fine-tuning approaches, requiring a decent amount of well-annotated training data to produce accurate diagnostic performance. This paper presents a novel incremental cross-domain adaptation instrument that allows any deep classification model to progress…
National University of Singapore（新加坡国立大学）；NUSRI Suzhou（新加坡国立大学苏州研究院）；Singapore General Hospital（新加坡中央医院）；The Chinese University of Hong Kong（香港中文大学）；Imperial College London（伦敦帝国理工学院）
Generating surgical reports aimed at surgical scene understanding in robot-assisted surgery can contribute to documenting entry tasks and post-operative analysis. Despite the impressive outcome, the deep learning model degrades the performance when applied to different domains encountering domain shifts. In addition, there are new instruments and variations in surgical tissues appeared in robotic surgery. In this work, we propose class-incremental domain adaptation (CIDA) with a multi-layer transformer-based model to tackle the new classes and domain shift in the target domain to generate surg…
This work presents an approach for incrementally updating deep neural network (DNN) models in a non-stationary environment. DNN models are sensitive to changes in input data distribution, which limits their application to problem settings with stationary input datasets. In a non-stationary environment, updating a DNN model requires parameter re-training or model fine-tuning. We propose an unsupervised source-free method to update DNN classification models. The contributions of this work are two-fold. First, we use trainable Gaussian prototypes to generate representative samples for future iter…
Vector Institute for AI（人工智能向量研究所）；Cheriton School of Computer Science, University of Waterloo（滑铁卢大学谢里顿计算机科学学院）；Dept. Computing Science, University of Alberta（阿尔伯塔大学计算机科学系）；Alberta Machine Intelligence Institute (AMII)（阿尔伯塔机器智能研究所（AMII））；Noah’s Ark Lab, Huawei Technologies（华为技术诺亚方舟实验室）
This paper addresses the problem of incremental domain adaptation (IDA) in natural language processing (NLP). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We adopt the recurrent neural network (RNN) widely used in NLP, but augment it with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new d…
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China（华南理工大学智能工程学院）；School of Future Technology, South China University of Technology, Guangzhou, China（华南理工大学未来技术学院）；Guangdong Laboratory of Artificial Intelligence（人工智能与数字经济广东省实验室）
SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
City University of Hong Kong（香港城市大学）；Harvard University（哈佛大学）；Xi’an Jiaotong University（西安交通大学）；Tencent AI Lab（腾讯AI实验室）；Zhejiang University（浙江大学）；Pengcheng Laboratory（鹏城实验室）
Gradient-based meta-learning has proven to be highly effective at learning model initializations, representations, and update rules that allow fast adaptation from a few samples. The core idea behind these approaches is to use fast adaptation and generalization -- two second-order metrics -- as training signals on a meta-training dataset. However, little attention has been given to other possible second-order metrics. In this paper, we investigate a different training signal -- robustness to catastrophic interference -- and demonstrate that representations learned by directing minimizing inter…
Traditional learning systems are trained in closed-world for a fixed number of classes, and need pre-collected datasets in advance. However, new classes often emerge in real-world applications and should be learned incrementally. For example, in electronic commerce, new types of products appear daily, and in a social media community, new topics emerge frequently. Under such circumstances, incremental models should learn several new classes at a time without forgetting. We find a strong correlation between old and new classes in incremental learning, which can be applied to relate and facilitat…
Few-shot Class-Incremental Learning via Generative Co-Memory Regularization
Institute of Information Engineering（信息工程研究所）；Chinese Academy of Sciences（中国科学院）；School of Cyber Security（网络安全学院）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Communication Engineering（通信工程系）
Cross-Domain Attribute Alignment with CLIP: A Rehearsal-Free Approach for Class-Incremental Unsupervised Domain Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Nanjing University of Science and Technology（南京理工大学）；School of Automation Science and Electrical Engineering（自动化科学与电气工程学院）；Beihang University（北航）；School of Automation and Intelligent Sensing（自动化与智能感知学院）；Shanghai Jiao Tong University（上海交通大学）
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces catastrophic forgetting and delays related to the retraining needed in order to incorpo- rate new classes. Here we introduce DeeSIL, an adaptation of a known transfer learning scheme that combines a fixed deep representation used as feature extractor and learning independent shallow classifiers to in- crease recognition capacity. This scheme t…
Suzhou University of Science and Technology（苏州科技大学）；Suzhou Institute of Trade & Commerce（苏州经贸职业技术学院）；Tianjin University（天津大学）
Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. Training on the data with Partial Labels in LML classification may result in more serious Catastrophic Forgetting in old classes. To solve the problem, the study proposes an Augmented Graph Convolutional Network (AGCN) with a built Augmented Correlation Matrix (ACM) across sequential partial…
University of Science and Technology of China（中国科学技术大学）；Shenzhen University（深圳大学）；The University of Birmingham（伯明翰大学）
Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data. Two design questions need to be addressed in developing ensemble methods for incremental learning with concept drift, i.e., which historical (i.e., previously trained) models should be preserved and how to utilize them. A novel ensemble learning method, namely Diversity and Transfer based Ensemble Learning (DTEL), is proposed in this paper. Given newly arrived data, DTEL uses each preserved historical model as an initial…
CLoRA: Parameter-Efficient Continual Learning with Low-Rank Adaptation
Augmented Vision Group, German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心增强视觉组）；RPTU – University of Kaiserslautern-Landau（凯撒斯劳滕-兰道大学）
Enhancing Plasticity for First Session Adaptation Continual Learning
LTCI, Télécom-Paris, Institut Polytechnique de Paris（Télécom-Paris 工程院）；University of Bergamo（贝加莫大学）；Inria at University Grenoble Alpes, LJK（格勒诺布尔阿尔卑斯大学）
Oxford Robotics Institute（牛津机器人研究所）；University of Oxford（牛津大学）
Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i.e. daytime, overcast) but performance degrades quickly and often catastrophically when input conditions change. In this work, we present a domain adaptation system that uses light-weight input adapters to pre-processes input images, irrespective of their appearance, in a way that makes them compatible with off-the-shelf computer vision tasks that are trained only on inputs with ideal conditions. No fine-tuning is performed on the off-the-shelf…
Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learnin…
RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；Department of Computer Science and Technology, Xiamen University（厦门大学计算机科学与技术系）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳MSU-BIT大学人工智能研究院）；School of Information, Central University of Finance and Economics（中央财经大学信息学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Deep learning has achieved notable success in 3D object detection with the advent of large-scale point cloud datasets. However, severe performance degradation in the past trained classes, i.e., catastrophic forgetting, still remains a critical issue for real-world deployment when the number of classes is unknown or may vary. Moreover, existing 3D class-incremental detection methods are developed for the single-domain scenario, which fail when encountering domain shift caused by different datasets, varying environments, etc. In this paper, we identify the unexplored yet valuable scenario, i.e.,…
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
Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilise semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlablled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring …
CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
Domain Adaptation (DA) techniques are important for overcoming the domain shift between the source domain used for training and the target domain where testing takes place. However, current DA methods assume that the entire target domain is available during adaptation, which may not hold in practice. This paper considers a more realistic scenario, where target data become available in smaller batches and adaptation on the entire target domain is not feasible. In our work, we introduce a new, data-constrained DA paradigm where unlabeled target samples are received in batches and adaptation is p…
University of Windsor（温莎大学）；Higher School of Communication of Tunis（突尼斯高等通信学院）
In this paper we outline a PhD research plan. This research contributes to the field of one-class incremental learning and classification in case of non-stationary environments. The goal of this PhD is to define a new classification framework able to deal with very small learning dataset at the beginning of the process and with abilities to adjust itself according to the variability of the incoming data which create large scale datasets. As a preliminary work, incremental Covariance-guided One-Class Support Vector Machine is proposed to deal with sequentially obtained data. It is inspired from…
