Results 21 to 30 of about 448,103 (274)
Deep Error-Correcting Output Codes
Algorithms, 2023 Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval.
Li-Na Wang +4 more
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Clustering-based Domain-Incremental Learning
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023 We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a ...
Lamers, C.H.C.B. +5 more
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Class Incremental Learning Method Integrating Balance Weight and Self-supervision [PDF]
Jisuanji kexue yu tansuoIn view of the catastrophic forgetting phenomenon of knowledge in class incremental learning in image classification, the existing class incremental learning methods focus on the correction of the unbalanced offset of the model classification layer ...
GONG Jiayi, XU Xinlei, XIAO Ting, WANG Zhe
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Survey of Federated Incremental Learning [PDF]
Jisuanji kexueFederated learning,with its unique distributed training mode and secure aggregation mechanism,has become a research hotspot in recent years.However,in real-life scenarios,local model training often faces new data,leading to catastrophic forgetting of old
XIE Jiachen, LIU Bo, LIN Weiwei , ZHENG Jianwen
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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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Incremental Learning Through Deep Adaptation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added domain, typically as many as the original network ...
Amir Rosenfeld, John K. Tsotsos
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Confidence Calibration for Incremental Learning
IEEE Access, 2020 Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks.
Dongmin Kang +3 more
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Using Domain Adaptation for Incremental SVM Classification of Drift Data
Mathematics, 2022 A common assumption in machine learning is that training data is complete, and the data distribution is fixed. However, in many practical applications, this assumption does not hold.
Junya Tang, Kuo-Yi Lin, Li Li
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Incremental Learning of Object Detectors without Catastrophic Forgetting [PDF]
, 2017 Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the ...
Alahari, Karteek +2 more
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Incremental semiparametric inverse dynamics learning [PDF]
2016 IEEE International Conference on Robotics and Automation (ICRA), 2016 This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system.
Raffaello Camoriano +4 more
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