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Generative Adversarial Networks in Time Series: A Systematic Literature Review
ACM Computing Surveys, 2023Eoin Brophy, Zhengwei Wang, Qi She
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Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity
ACM Computing Surveys, 2023Shuai Zhou, Chi Liu, Dayong Ye
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Interpreting Adversarial Examples in Deep Learning: A Review
ACM Computing Surveys, 2023Sicong Han, Chenhao Lin, Chao Shen
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How Generative Adversarial Networks and Their Variants Work
ACM Computing Surveys, 2020Youngjun Hong, Uiwon Hwang, Sungroh Yoon
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Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Takeru Miyato, Shin Ishii
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Adversarial examples in machine learning
Diese Arbeit untersucht die mathematischen Grundlagen zur Generierung sogenannter Adversarial Examples und entwickelt Methoden zum Schutz von Modellen des maschinellen Lernens gegen solche gezielten Störungen. Das Konzept, bekannt als Adversarial Risk, beschreibt die Robustheit eines Modells als Min-Max-Optimierungsproblem.openaire +1 more source
Generative adversarial network in medical imaging: A review
Medical Image Analysis, 2019Xin Yi, Ekta Walia, Paul Babyn
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