Results 21 to 30 of about 151,060 (281)
Incremental Learning for Online Data Using QR Factorization on Convolutional Neural Networks
Sensors, 2023 Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the
Jonghong Kim +4 more
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Catastrophic Forgetting in the Context of Model Updates
CoRR, 2023 A large obstacle to deploying deep learning models in practice is the process of updating models post-deployment (ideally, frequently). Deep neural networks can cost many thousands of dollars to train. When new data comes in the pipeline, you can train a new model from scratch (randomly initialized weights) on all existing data.
Richard E. Harang, Hillary Sanders
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Enhancing network modularity to mitigate catastrophic forgetting
Applied Network Science, 2020 Catastrophic forgetting occurs when learning algorithms change connections used to encode previously acquired skills to learn a new skill. Recently, a modular approach for neural networks was deemed necessary as learning problems grow in scale and ...
Lu Chen, Masayuki Murata
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Learning Without Forgetting: A New Framework for Network Cyber Security Threat Detection
IEEE Access, 2021 Progressive learning addresses the problem of incrementally learning new tasks without compromising the prediction accuracy of previously learned tasks. In the context of artificial neural networks, several algorithms exist for achieving the progressive ...
Rupesh Raj Karn +2 more
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Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
, 2022 14 pages including appendix, 5 figures, 2 tables, 1 algorithm listing. v2 update increases figure readability, updates Fig 5 caption, adds our collaborators Dario and An as co-authors v3 brings the preprint in line with the final version accepted for peer-reviewed publication at CD-MAKE 2022.
Sami Ede +6 more
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Combating catastrophic forgetting with developmental compression [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2018 Generally intelligent agents exhibit successful behavior across problems in several settings. Endemic in approaches to realize such intelligence in machines is catastrophic forgetting: sequential learning corrupts knowledge obtained earlier in the sequence, or tasks antagonistically compete for system resources.
Shawn L. E. Beaulieu +2 more
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Addressing Catastrophic Forgetting in Few-Shot Problems [PDF]
, 2021 Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets. While there have been numerous attempts to solve this problem in large-scale supervised classification, little has been done to overcome catastrophic ...
Ritter, Hippolyt +2 more
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Catastrophic Forgetting Problem in Semi-Supervised Semantic Segmentation
IEEE Access, 2022 Restricted by the cost of generating labels for training, semi-supervised methods have been applied to semantic segmentation tasks and have achieved varying degrees of success. Recently, the semi-supervised learning method has taken pseudo supervision as
Yan Zhou +4 more
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A divided and prioritized experience replay approach for streaming regression
MethodsX, 2021 In the streaming learning setting, an agent is presented with a data stream on which to learn from in an online fashion. A common problem is catastrophic forgetting of old knowledge due to updates to the model.
Mikkel Leite Arnø +2 more
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Forgiving you is hard, but forgetting seems easy : can forgiveness facilitate forgetting? [PDF]
, 2014 Forgiveness is considered to play a key role in the maintenance of social relationships, the avoidance of unnecessary conflict, and the ability to move forward with one’s life.
Bierman, Raynett +6 more
core +1 more source