Littérature scientifique sur le sujet « Class-incremental learning »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Class-incremental learning ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Class-incremental learning"
Kim, Taehoon, Jaeyoo Park et Bohyung Han. « Cross-Class Feature Augmentation for Class Incremental Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 12 (24 mars 2024) : 13168–76. http://dx.doi.org/10.1609/aaai.v38i12.29216.
Texte intégralPark, Ju-Youn, et Jong-Hwan Kim. « Incremental Class Learning for Hierarchical Classification ». IEEE Transactions on Cybernetics 50, no 1 (janvier 2020) : 178–89. http://dx.doi.org/10.1109/tcyb.2018.2866869.
Texte intégralQin, Yuping, Hamid Reza Karimi, Dan Li, Shuxian Lun et Aihua Zhang. « A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm ». Abstract and Applied Analysis 2014 (2014) : 1–5. http://dx.doi.org/10.1155/2014/894246.
Texte intégralPang, Shaoning, Lei Zhu, Gang Chen, Abdolhossein Sarrafzadeh, Tao Ban et Daisuke Inoue. « Dynamic class imbalance learning for incremental LPSVM ». Neural Networks 44 (août 2013) : 87–100. http://dx.doi.org/10.1016/j.neunet.2013.02.007.
Texte intégralLiu, Yaoyao, Yingying Li, Bernt Schiele et Qianru Sun. « Online Hyperparameter Optimization for Class-Incremental Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 7 (26 juin 2023) : 8906–13. http://dx.doi.org/10.1609/aaai.v37i7.26070.
Texte intégralZhang, Lijuan, Xiaokang Yang, Kai Zhang, Yong Li, Fu Li, Jun Li et Dongming Li. « Class-Incremental Learning Based on Anomaly Detection ». IEEE Access 11 (2023) : 69423–38. http://dx.doi.org/10.1109/access.2023.3293524.
Texte intégralLiang, Sen, Kai Zhu, Wei Zhai, Zhiheng Liu et Yang Cao. « Hypercorrelation Evolution for Video Class-Incremental Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 4 (24 mars 2024) : 3315–23. http://dx.doi.org/10.1609/aaai.v38i4.28117.
Texte intégralXu, Shixiong, Gaofeng Meng, Xing Nie, Bolin Ni, Bin Fan et Shiming Xiang. « Defying Imbalanced Forgetting in Class Incremental Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 14 (24 mars 2024) : 16211–19. http://dx.doi.org/10.1609/aaai.v38i14.29555.
Texte intégralGuo, Jiaqi, Guanqiu Qi, Shuiqing Xie et Xiangyuan Li. « Two-Branch Attention Learning for Fine-Grained Class Incremental Learning ». Electronics 10, no 23 (1 décembre 2021) : 2987. http://dx.doi.org/10.3390/electronics10232987.
Texte intégralQin, Zhili, Wei Han, Jiaming Liu, Rui Zhang, Qingli Yang, Zejun Sun et Junming Shao. « Rethinking few-shot class-incremental learning : A lazy learning baseline ». Expert Systems with Applications 250 (septembre 2024) : 123848. http://dx.doi.org/10.1016/j.eswa.2024.123848.
Texte intégralThèses sur le sujet "Class-incremental learning"
Hocquet, Guillaume. « Class Incremental Continual Learning in Deep Neural Networks ». Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST070.
Texte intégralWe are interested in the problem of continual learning of artificial neural networks in the case where the data are available for only one class at a time. To address the problem of catastrophic forgetting that restrain the learning performances in these conditions, we propose an approach based on the representation of the data of a class by a normal distribution. The transformations associated with these representations are performed using invertible neural networks, which can be trained with the data of a single class. Each class is assigned a network that will model its features. In this setting, predicting the class of a sample corresponds to identifying the network that best fit the sample. The advantage of such an approach is that once a network is trained, it is no longer necessary to update it later, as each network is independent of the others. It is this particularly advantageous property that sets our method apart from previous work in this area. We support our demonstration with experiments performed on various datasets and show that our approach performs favorably compared to the state of the art. Subsequently, we propose to optimize our approach by reducing its impact on memory by factoring the network parameters. It is then possible to significantly reduce the storage cost of these networks with a limited performance loss. Finally, we also study strategies to produce efficient feature extractor models for continual learning and we show their relevance compared to the networks traditionally used for continual learning
Júnior, João Roberto Bertini. « Classificação de dados estacionários e não estacionários baseada em grafos ». Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-15032011-102039/.
Texte intégralGraph-based methods consist in a powerful form for data representation and abstraction which provides, among others advantages, representing topological relations, visualizing structures, representing groups of data with distinct formats, as well as, supplying alternative measures to characterize data. Such approach has been each time more considered to solve machine learning related problems, mainly concerning unsupervised learning, like clustering, and recently, semi-supervised learning. However, graph-based solutions for supervised learning tasks still remain underexplored in literature. This work presents a non-parametric graph-based algorithm suitable for classification problems with stationary distribution, as well as its extension to cope with problems of non-stationary distributed data. The developed algorithm relies on the following concepts, 1) a graph structure called optimal K-associated graph, which represents the training set as a sparse graph separated into components; and 2) the purity measure for each component, which uses the graph structure to determine local data mixture level in relation to their classes. This work also considers classification problems that exhibit modification on distribution of data flow. This problem qualifies concept drift and worsens any static classifier performance. Hence, in order to maintain accuracy performance, it is necessary for the classifier to keep learning during application phase, for example, by implementing incremental learning. Experimental results, concerning both algorithms, suggest that they had presented advantages over the tested algorithms on data classification tasks
Ngo, Ho Anh Khoi. « Méthodes de classifications dynamiques et incrémentales : application à la numérisation cognitive d'images de documents ». Thesis, Tours, 2015. http://www.theses.fr/2015TOUR4006/document.
Texte intégralThis research contributes to the field of dynamic learning and classification in case of stationary and 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 inside a stream. For that purpose, we propose a solution based on a combination of independent one-class SVM classifiers having each one their own incremental learning procedure. Consequently, each classifier is not sensitive to crossed influences which can emanate from the configuration of the models of the other classifiers. The originality of our proposal comes from the use of the former knowledge kept in the SVM models (represented by all the found support vectors) and its combination with the new data coming incrementally from the stream. The proposed classification model (mOC-iSVM) is exploited through three variations in the way of using the existing models at each step of time. Our contribution states in a state of the art where no solution is proposed today to handle at the same time, the concept drift, the addition or the deletion of concepts, the fusion or division of concepts while offering a privileged solution for interaction with the user. Inside the DIGIDOC project, our approach was applied to several scenarios of classification of images streams which can correspond to real cases in digitalization projects. These different scenarios allow validating an interactive exploitation of our solution of incremental classification to classify images coming in a stream in order to improve the quality of the digitized images
Daou, Andrea. « Real-time Indoor Localization with Embedded Computer Vision and Deep Learning ». Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR002.
Texte intégralThe need to determine the location of individuals or objects in indoor environments has become an essential requirement. The Global Navigation Satellite System, a predominant outdoor localization solution, encounters limitations when applied indoors due to signal reflections and attenuation caused by obstacles. To address this, various indoor localization solutions have been explored. Wireless-based indoor localization methods exploit wireless signals to determine a device's indoor location. However, signal interference, often caused by physical obstructions, reflections, and competing devices, can lead to inaccuracies in location estimation. Additionally, these methods require access points deployment, incurring associated costs and maintenance efforts. An alternative approach is dead reckoning, which estimates a user's movement using a device's inertial sensors. However, this method faces challenges related to sensor accuracy, user characteristics, and temporal drift. Other indoor localization techniques exploit magnetic fields generated by the Earth and metal structures. These techniques depend on the used devices and sensors as well as the user's surroundings.The goal of this thesis is to provide an indoor localization system designed for professionals, such as firefighters, police officers, and lone workers, who require precise and robust positioning solutions in challenging indoor environments. In this thesis, we propose a vision-based indoor localization system that leverages recent advances in computer vision to determine the location of a person within indoor spaces. We develop a room-level indoor localization system based on Deep Learning (DL) and built-in smartphone sensors combining visual information with smartphone magnetic heading. To achieve localization, the user captures an image of the indoor surroundings using a smartphone, equipped with a camera, an accelerometer, and a magnetometer. The captured image is then processed using our proposed multiple direction-driven Convolutional Neural Networks to accurately predict the specific indoor room. The proposed system requires minimal infrastructure and provides accurate localization. In addition, we highlight the importance of ongoing maintenance of the vision-based indoor localization system. This system necessitates regular maintenance to adapt to changing indoor environments, particularly when new rooms have to be integrated into the existing localization framework. Class-Incremental Learning (Class-IL) is a computer vision approach that allows deep neural networks to incorporate new classes over time without forgetting the knowledge previously learned. In the context of vision-based indoor localization, this concept must be applied to accommodate new rooms. The selection of representative samples is essential to control memory limits, avoid forgetting, and retain knowledge from previous classes. We develop a coherence-based sample selection method for Class-IL, bringing forward the advantages of the coherence measure to a DL framework. The relevance of the methodology and algorithmic contributions of this thesis is rigorously tested and validated through comprehensive experimentation and evaluations on real datasets
Bruni, Matteo. « Incremental Learning of Stationary Representations ». Doctoral thesis, 2021. http://hdl.handle.net/2158/1237986.
Texte intégralMandal, Devraj. « Cross-Modal Retrieval and Hashing ». Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4685.
Texte intégralChapitres de livres sur le sujet "Class-incremental learning"
Tao, Xiaoyu, Xinyuan Chang, Xiaopeng Hong, Xing Wei et Yihong Gong. « Topology-Preserving Class-Incremental Learning ». Dans Computer Vision – ECCV 2020, 254–70. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58529-7_16.
Texte intégralLiu, Xialei, Yu-Song Hu, Xu-Sheng Cao, Andrew D. Bagdanov, Ke Li et Ming-Ming Cheng. « Long-Tailed Class Incremental Learning ». Dans Lecture Notes in Computer Science, 495–512. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19827-4_29.
Texte intégralde Carvalho, Marcus, Mahardhika Pratama, Jie Zhang et Yajuan Sun. « Class-Incremental Learning via Knowledge Amalgamation ». Dans Machine Learning and Knowledge Discovery in Databases, 36–50. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_3.
Texte intégralYang, Dejie, Minghang Zheng, Weishuai Wang, Sizhe Li et Yang Liu. « Recent Advances in Class-Incremental Learning ». Dans Lecture Notes in Computer Science, 212–24. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46308-2_18.
Texte intégralBelouadah, Eden, Adrian Popescu, Umang Aggarwal et Léo Saci. « Active Class Incremental Learning for Imbalanced Datasets ». Dans Computer Vision – ECCV 2020 Workshops, 146–62. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65414-6_12.
Texte intégralAyromlou, Sana, Purang Abolmaesumi, Teresa Tsang et Xiaoxiao Li. « Class Impression for Data-Free Incremental Learning ». Dans Lecture Notes in Computer Science, 320–29. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16440-8_31.
Texte intégralZhang, Zhenyao, et Lijun Zhang. « NeCa : Network Calibration for Class Incremental Learning ». Dans Lecture Notes in Computer Science, 385–99. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47634-1_29.
Texte intégralEr, Meng Joo, Vijaya Krishna Yalavarthi, Ning Wang et Rajasekar Venkatesan. « A Novel Incremental Class Learning Technique for Multi-class Classification ». Dans Advances in Neural Networks – ISNN 2016, 474–81. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40663-3_54.
Texte intégralElskhawy, Abdelrahman, Aneta Lisowska, Matthias Keicher, Joseph Henry, Paul Thomson et Nassir Navab. « Continual Class Incremental Learning for CT Thoracic Segmentation ». Dans Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 106–16. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60548-3_11.
Texte intégralLei, Cheng-Hsun, Yi-Hsin Chen, Wen-Hsiao Peng et Wei-Chen Chiu. « Class-Incremental Learning with Rectified Feature-Graph Preservation ». Dans Computer Vision – ACCV 2020, 358–74. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_22.
Texte intégralActes de conférences sur le sujet "Class-incremental learning"
Luo, Zilin, Yaoyao Liu, Bernt Schiele et Qianru Sun. « Class-Incremental Exemplar Compression for Class-Incremental Learning ». Dans 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01094.
Texte intégralMi, Fei, Lingjing Kong, Tao Lin, Kaicheng Yu et Boi Faltings. « Generalized Class Incremental Learning ». Dans 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00128.
Texte intégralTao, Qingyi, Chen Change Loy, Jianfei Cad, Zongyuan Get et Simon See. « Retrospective Class Incremental Learning ». Dans 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2021. http://dx.doi.org/10.1109/icme51207.2021.9428257.
Texte intégralDong, Jiahua, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang et Qi Zhu. « Federated Class-Incremental Learning ». Dans 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00992.
Texte intégralTao, Xiaoyu, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei et Yihong Gong. « Few-Shot Class-Incremental Learning ». Dans 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01220.
Texte intégralLechat, Alexis, Stephane Herbin et Frederic Jurie. « Semi-Supervised Class Incremental Learning ». Dans 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413225.
Texte intégralMittal, Sudhanshu, Silvio Galesso et Thomas Brox. « Essentials for Class Incremental Learning ». Dans 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00390.
Texte intégralPian, Weiguo, Shentong Mo, Yunhui Guo et Yapeng Tian. « Audio-Visual Class-Incremental Learning ». Dans 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00717.
Texte intégralWang, Wei, Zhiying Zhang et Jielong Guo. « Brain-inspired Class Incremental Learning ». Dans 2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2022. http://dx.doi.org/10.1109/iciscae55891.2022.9927584.
Texte intégralHan, Ruizhi, C. L. Philip Chen et Shuang Feng. « Broad Learning System for Class Incremental Learning ». Dans 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2018. http://dx.doi.org/10.1109/spac46244.2018.8965551.
Texte intégral