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

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Fair Data Generation and Machine Learning Through Generative Adversarial Networks,e FairGAN framework can accommodate various fairness notions by changing the network architecture and objective functions of generators and discriminators. Under the FairGAN framework, we present three previously published model designs, Simplified-FairGAN [.], Causal-FairGAN [.], and FairGAN. [.],

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Quaternion Generative Adversarial Networks,ions of parameters requiring extensive computational capabilities. Building such huge models undermines their replicability and increases the training instability. Moreover, multi-channel data, such as images or audio, are usually processed by real-valued convolutional networks that flatten and conc

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Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Inteetection. This chapter studies a number of GAN architectures used for anomaly detection in the data stream. Moreover, a novel approach is proposed for embedding the dynamic characteristics of the data stream into the GAN-based detector structures. In this process, a GAN model is also proposed for ef

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Inspection of Lead Frame Defects Using Deep CNN and Cycle-Consistent GAN-Based Defect Augmentation,y. 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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Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition,ecognition 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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,GANs for?Molecule Generation in?Drug Design and?Discovery,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