دانلود کتاب معماری ها و کاربردهای یادگیری خصمانه مولد

The recent big advancements in Generative Adversarial Networks (GANs) have opened countless opportunities for synthetic data generation inmany areas, including science, engineering, medicine, security, among others. Generative Adversarial Networks are built based on two dueling neural networks, in which one network aims to generate newsamples and convince the competing network that the generated samples are real and not synthetic. This book contains recent theoretical advancements on Generative Adversarial Networks and their interdisciplinary applications, such as for fair data generation, image generation, face aging, intrusion detection, industrial defect detection, action recognition, drug discovery, medical imaging, among other applications. The book is intended for scientists, researchers, educators, and anyone interested in the latest methodological advancements on Generative Adversarial Networks and their applications. In addition, the book can be considered as a reference for various state-of-the-art architectures of GANs, major training challenges of GANs including convergence, vanishing gradients, and mode collapse, as well as the technical remedies to overcome these challenges.