Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.
- Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN
- Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks
- Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis
- Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications
Biomedical Engineers and researchers in biomedical engineering, applied informatics, Artificial Intelligence, and data science. Students and researchers in data analytics, image processing, as well as computer scientists
Section 1: Introductory Concepts of GAN Network
1. Basics of Generative Modeling
2. Advanced Topics in GANs
3. Deep Learning
Section 2: Various Types of GAN Network
5. Generative Adversarial Networks
6. Generation of handwriting digits
7. GAN implementation in Keras
8. Teaching machines to paint, write and Play
9. Future of Generative Networks
Section 3: Real-World Applications Using GAN
10. Deep convolution GAN
11. Training challenges in GAN
12. GAN in medicine
13. GAN in fashion
- No. of pages:
- © Academic Press 2021
- 1st June 2021
- Academic Press
- Paperback ISBN:
Dr. Arun Solanki is Assistant Professor in the Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India. He received his Ph.D. in Computer Science and Engineering from Gautam Buddha University. He has supervised more than 60 M.Tech. Dissertations under his guidance. His research interests span Expert System, Machine Learning, and Search Engines. Dr. Solanki is an Associate Editor of the International Journal of Web-Based Learning and Teaching Technologies from IGI Global. He has been a Guest Editor for special issues of Recent Patents on Computer Science, from Bentham Science Publishers. Dr. Solanki is the editor of the books Green Building Management and Smart Automation and Handbook of Emerging Trends and Applications of Machine Learning, both from IGI Global.
Assistant Professor, Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India
Dr. Anand Nayyar received his Ph.D in Computer Science from Desh Bhagat University in the area of Wireless Sensor Networks. He is currently working as a Researcher at Duy Tan University, Da Nang, Vietnam. He holds more than 75 professional certificates from corporations and organizations such as CISCO, Microsoft, Oracle, Google, EXIN, and GAQM. His current research interests are in the areas of Wireless Sensor Networks, MANETS, Swarm Intelligence, Cloud Computing, Internet of Things, Blockchain, Machine Learning, Deep Learning, Cyber Security, Network Simulation, and Wireless Communications. Dr. Nayyar is the editor of the books Green Building Management and Smart Automation from IGI Global, A Roadmap to Industry 4.0 from Springer, Handbook of Cloud Computing from BPB Publications, Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development from CRC Press, and Advances in Swarm Intelligence for Optimizing Problems in Computer Science from CRC Press, among others. He was recently signed as part of the Editor team for our title Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics.
Professor, Researcher and Scientist, Graduate School, Duy Tan University, Da Nang, Vietnam
Dr. Mohd Naved is a machine learning consultant and academician, currently teaching as Assistant Professor and HoD (Analytics & IB) in Jagannath University in collaboration with Xcelerator Ninja (India) for various UG & PG programs in Analytics and Machine Learning. A former data scientist and an alumnus of Delhi University. He holds a PhD from Noida International University. He is actively engaged in academic research on various topics in artificial intelligences and 21st century technologies. His interviews have been published in various national and international magazines
Assistant Professor and HoD (Analytics and IB), Jagannath University; Various UG & PG programs, Analytics and Machine Learning Xcelerator Ninja, India
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.