Titlebook: Generative Adversarial Learning: Architectures and Applications; Roozbeh Razavi-Far,Ariel Ruiz-Garcia,Juergen Schmi Book 2022 The Editor(s

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Cyber Threats (and Opportunities),d. Traditionally, there have been two kinds of modeling techniques used in this task: prototype-based and model-based methods. The first calculates the mean difference between age groups, and the latter uses parametric models to simulate change over time. Both approaches fail to keep individual char

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Artificial Intelligence and Data Miningy. A lead frame is a thin layer of metal inside a chip package connecting a die to the circuitry on circuit boards. This chapter introduces the application of the faster region-based convolutional neural network (R-CNN) to detect and classify the defects on lead frames using AlexNet as a backbone. A

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https://doi.org/10.1007/978-3-031-54184-1ecognition of human activities from smartphone sensors, when limited training data is available. Generative Adversarial Networks (GANs) provide an approach to model the distribution of a dataset and can be used to augment data to reduce the amount of labelled data required to train accurate classifi

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Chancen und Risiken privater Firmen,rate novel molecules to build a virtual molecule library for further screening. With the rapid development of deep generative modeling techniques, researchers are now applying deep generative models, particularly Generative Adversarial Networks (GANs), for molecule generation. In this chapter, we tr

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https://doi.org/10.1007/978-3-030-37802-8ce of radiation, superior soft tissue contrast, and complementary multiple sequence information. However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical. Trad