Results 21 to 30 of about 3,000 (250)
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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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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Overcoming Catastrophic Forgetting by XAI
CoRR, 2022 Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain ...
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Behavioral Experiments for Understanding Catastrophic Forgetting
CoRR, 2021 In this paper we explore whether the fundamental tool of experimental psychology, the behavioral experiment, has the power to generate insight not only into humans and animals, but artificial systems too. We apply the techniques of experimental psychology to investigating catastrophic forgetting in neural networks.
Samuel J. Bell, Neil D. Lawrence
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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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Embodiment can combat catastrophic forgetting [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018 We use an evolutionary robotics approach to demonstrate how the choice of robot morphology can affect one specific aspect of neural networks: their ability to resist catastrophic forgetting.
Joshua P. Powers +2 more
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Natural Way to Overcome Catastrophic Forgetting in Neural Networks
Современные информационные технологии и IT-образование, 2020 The problem of catastrophic forgetting manifested itself in models of neural networks based on the connectionist approach, which have been actively studied since the second half of the 20th century.
Alexey Kutalev
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IEEE Access, 2023 Continual learning aims to enable neural networks to learn new tasks without catastrophic forgetting of previously learned knowledge. Orthogonal Gradient Descent algorithms have been proposed as an effective solution to mitigate catastrophic forgetting ...
Da Eun Lee +3 more
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