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Semi-Supervised Class Incremental Learning [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 This paper makes a contribution to the problem of incremental class learning, the principle of which is to sequentially introduce batches of samples annotated with new classes during the learning phase. The main objective is to reduce the drop in classification performance on old classes, a phenomenon commonly called catastrophic forgetting. We propose
Alexis Lechat +2 more
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Incrementally Learned Angular Representations for Few-Shot Class-Incremental Learning
IEEE Access, 2023 The main challenge of FSCIL is the trade-off between underfitting to a new session task and preventing forgetting the knowledge for earlier sessions. In this paper, we reveal that the angular space occupied by the features within the embedded area is ...
In-Ug Yoon, Jong-Hwan Kim
doaj +1 more source
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2022 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 ...
Kai Zhu +4 more
semanticscholar +1 more source
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022 Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental process is needed in order to obtain good accuracy for past as well as new ...
Grégoire Petit +4 more
semanticscholar +1 more source
An Optimized Class Incremental Learning Network with Dynamic Backbone Based on Sonar Images
Journal of Marine Science and Engineering, 2023 Class incremental learning with sonar images introduces a new dimension to underwater target recognition. Directly applying networks designed for optical images to our constructed sonar image dataset (SonarImage20) results in significant catastrophic ...
Xinzhe Chen, Hong Liang
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Class-Incremental Learning: Survey and Performance Evaluation on Image Classification [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data ...
Marc Masana +5 more
semanticscholar +1 more source
Prototypes Sampling Mechanism for Class Incremental Learning
IEEE Access, 2023 Incremental learning aims to alleviate the catastrophic forgetting problem of deep neural networks during learning sequential data stream. This problem is even more challenging when old data is unavailable, since learning system can only be trained under
Zhe Tao, Shucheng Huang, Gang Wang
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Incremental Cost-Sensitive Support Vector Machine With Linear-Exponential Loss
IEEE Access, 2020 Incremental learning or online learning as a branch of machine learning has attracted more attention recently. For large-scale problems and dynamic data problem, incremental learning overwhelms batch learning, because of its efficient treatment for new ...
Yue Ma, Kun Zhao, Qi Wang, Yingjie Tian
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AdvisIL - A Class-Incremental Learning Advisor
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 Recent class-incremental learning methods combine deep neural architectures and learning algorithms to handle streaming data under memory and computational constraints. The performance of existing methods varies depending on the characteristics of the incremental process. To date, there is no other approach than to test all pairs of learning algorithms
Feillet, Eva +4 more
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Is Class-Incremental Enough for Continual Learning?
Frontiers in Artificial Intelligence, 2022 The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of the learning environment.
Andrea Cossu +7 more
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