Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels

Language: English

Published by Apprimus Verlag

3863598024 / 9783863598020

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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. [.].

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Bibliographic details

Title
Robustness Analysis of Deep Neural Networks in the Presence of Adversarial Perturbations and Noisy Labels
Author
Emilio Balda
Publisher
Apprimus Verlag
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3863598024
ISBN 13
9783863598020
Item weight
258 grams
Dimensions
219x158x10 mm

AHA-BUCH GmbH

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