Generative Adversarial Learning: Architectures and Applications

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.

• Generative Adversarial Networks
• Deep Learning
• Artificial Intelligence
• Neural Networks
• Machine Learning
• Data Augmentation
• Data Synthesis

• Department of Electrical and Computer Engineering and School of Computer Science, University of Windsor, Windsor, Canada

  Roozbeh Razavi-Far

• SeeChange.ai, Manchester, UK

  Ariel Ruiz-Garcia

• Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, Switzerland

  Vasile Palade

• The Swiss AI Lab, IDSIA, University of Lugano, USI & SUPSI, Lugano, Switzerland

  Juergen Schmidhuber