Generative Adversarial Networks for Image-to-Image Translation

Generative Adversarial Networks (GAN) have revolutionized Deep Learning, enabling image-to-image translation and a wide range of real-world applications. GANs consist of two neural networks, a generator and a discriminator, that compete to generate high-quality images. They are particularly useful in cases where labeled data is not available, allowing for the generation of images, faces, music, speech, and more.

Format: Paperback / softback
Length: 444 pages
Publication date: 23 June 2021
Publisher: Elsevier Science Publishing Co Inc

Generative Adversarial Networks (GAN) have sparked a revolutionary transformation in the realm of Deep Learning, and today, GAN stands as one of the most extensively researched topics in the field of Artificial Intelligence. In "Generative Adversarial Networks for Image-to-Image Translation," readers are offered a comprehensive exploration of the GAN concept, spanning from its original inception to various GAN-based systems, including Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and numerous other advancements. This book also delves into practical real-world applications and showcases common projects constructed using the GAN system, accompanied by corresponding Python code.

A standard GAN system comprises two neural networks, namely the generator and the discriminator. These two networks engage in a competitive dynamic akin to game theory. The generator's primary responsibility is to generate high-quality images that closely resemble the true ground truth. On the other hand, the discriminator's role is to determine whether a generated image is authentic or a counterfeit creation of the generator. As an architecture rooted in unsupervised learning, GAN emerges as a preferred choice when labeled data is scarce. It possesses remarkable capabilities, such as generating high-quality images, creating images of human faces derived from multiple sketches, converting images between domains, enhancing images, blending images with distinct styles, altering the appearance of human face images to simulate aging effects, generating images from text, and many more innovative applications.

One of the most remarkable aspects of GAN is its ability to produce output that closely resembles the output generated by humans with remarkable speed. It can efficiently generate high-quality music, speech, and images, surpassing traditional methods in terms of efficiency and quality. The GAN architecture has opened up new avenues for research and development in the field of Artificial Intelligence, enabling scientists and researchers to explore the boundaries of image generation, natural language processing, and other domains.

In conclusion, "Generative Adversarial Networks for Image-to-Image Translation" serves as an invaluable resource for anyone seeking to delve into the world of GANs. It provides a comprehensive introduction to the concept, explores various GAN-based systems, and showcases practical applications in real-world scenarios. With its detailed explanations, accompanied by Python code, this book empowers readers to build their own GAN-based projects and unlock the full potential of this transformative technology.

Weight: 918g
Dimension: 192 x 233 x 25 (mm)
ISBN-13: 9780128235195