An Introduction to Generative Adversarial Learning: Architectures and Applications

Generative Adversarial Networks (GANs) continue to be one of the most popular deep learning approaches for synthetic data generation. Moreover, GANs are finding their way in other application domains, such as speech and audio synthesis, object detection and segmentation, text-to-image translation, and policy learning in deep reinforcement learning, among many others. This book presents a collection of chapters on the latest advancements on GANs and state-of-the-art applications.