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- W4281401336 abstract "Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to provide an analysis on these two sides of adversarial learning and harness their potential for improving the robustness and generalization of deep models." @default.
- W4281401336 created "2022-05-25" @default.
- W4281401336 creator A5084719864 @default.
- W4281401336 date "2022-05-24" @default.
- W4281401336 modified "2023-09-26" @default.
- W4281401336 title "An Analysis on Adversarial Machine Learning: Methods and Applications" @default.
- W4281401336 doi "https://doi.org/10.33915/etd.11250" @default.
- W4281401336 hasPublicationYear "2022" @default.
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