Results 31 to 40 of about 151,060 (281)
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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Catastrophic Forgetting: Still a Problem for DNNs [PDF]
, 2018 We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning.
Benedikt Pfülb +3 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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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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CL3: Generalization of Contrastive Loss for Lifelong Learning
Journal of Imaging, 2023 Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience.
Kaushik Roy +3 more
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ERP correlates of forgetting: an investigation of resource allocation as a potential neural mechanism behind retrieval-induced forgetting [PDF]
, 2012 The present study was aimed at investigating a potential mechanism behind retrieval-induced forgetting that we have termed resource allocation. Three experiments were designed around the notion that increasing the number memories associated with one ...
Walters, Marie
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Quantum Continual Learning Overcoming Catastrophic Forgetting
, 2021 Catastrophic forgetting describes the fact that machine learning models will likely forget the knowledge of previously learned tasks after the learning process of a new one.
Lu, Zhide +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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Continual Learning With Speculative Backpropagation and Activation History
IEEE Access, 2022 Continual learning is gaining traction these days with the explosive emergence of deep learning applications. Continual learning suffers from a severe problem called catastrophic forgetting.
Sangwoo Park, Taeweon Suh
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