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Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels - Softcover
- ISBN 10 3863598024
- ISBN 13 9783863598020
- BindingPerfect Paperback
- LanguageEnglish
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Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels
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Apprimus Verlag Jan 2020, 2020
ISBN 10: 3863598024
ISBN 13: 9783863598020
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this thesis, we study the robustness and generalization properties of Deep Neural Networks (DNNs) under various noisy regimes, due to corrupted inputs or labels. Such corruptions can be either random or intentionally crafted to disturb the target DNN. Inputs corrupted by maliciously designed perturbations are known as adversarial examples and have been shown to severely degrade the performance of DNNs. However, due to the non-linearity of DNNs, crafting such perturbations is non-trivial. [.] 134 pp. Englisch. Seller Inventory # 9783863598020
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Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this thesis, we study the robustness and generalization properties of Deep Neural Networks (DNNs) under various noisy regimes, due to corrupted inputs or labels. Such corruptions can be either random or intentionally crafted to disturb the target DNN. Inputs corrupted by maliciously designed perturbations are known as adversarial examples and have been shown to severely degrade the performance of DNNs. However, due to the non-linearity of DNNs, crafting such perturbations is non-trivial. [.]. Seller Inventory # 9783863598020
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Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels
Print on DemandSeller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this thesis, we study the robustness and generalization properties of Deep Neural Networks (DNNs) under various noisy regimes, due to corrupted inputs or labels. Such corruptions can be either random or intentionally crafted to disturb the target DNN. In. Seller Inventory # 341525380
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Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels
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Taschenbuch. Condition: Neu. Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels | Emilio Balda | Taschenbuch | Großformatiges Paperback. Klappenbroschur | Englisch | Apprimus Verlag | EAN 9783863598020 | Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 117945229
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