Razavi-Far R.Generative Adversarial Learning. Architect.App 2022

Textbook in PDF format

This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
An Introduction to Generative Adversarial Learning: Architectures and Applications
Generative Adversarial Networks: A Survey on Training, Variants, and Applications
Fair Data Generation and Machine Learning Through Generative Adversarial Networks
Quaternion Generative Adversarial Networks
Image Generation Using Continuous Conditional Generative Adversarial Networks
Generative Adversarial Networks for Data Augmentation in Hyperspectral Image Classification
Face Aging Using Generative Adversarial Networks
Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks
Inspection of Lead Frame Defects Using Deep CNN and Cycle-Consistent GAN-Based Defect Augmentation
Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition
GANs for Molecule Generation in Drug Design and Discovery
Improved Diagnostic Performance of Arrhythmia Classification Using Conditional GAN Augmented Heartbeats
Generative Adversarial Network Powered Fast Magnetic Resonance Imaging—Comparative Study and New Perspectives
Generative Adversarial Networks for Data Augmentation in X-Ray Medical Imaging