Results 21 to 30 of about 33,100 (246)
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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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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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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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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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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On Robustness of Generative Representations Against Catastrophic Forgetting [PDF]
, 2021 Catastrophic forgetting of previously learned knowledge while learning new tasks is a widely observed limitation of contemporary neural networks. Although many continual learning methods are proposed to mitigate this drawback, the main question remains unanswered: what is the root cause of catastrophic forgetting? In this work, we aim at answering this
Wojciech Masarczyk +2 more
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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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