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Generative Adversarial Networks for Image-to-Image Translation
1st Edition - June 22, 2021
Editors: Arun Solanki, Anand Nayyar, Mohd Naved
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 5 1 9 - 5
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 6 1 3 - 0
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… Read more
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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
1. Super-Resolution based GAN for Image Processing: Recent Advances and Future Trends
2. GAN models in Natural Language Processing and Image Translation
3. Generative Adversarial Networks and their variants
4. Comparative Analysis of Filtering Methods in Fuzzy C-Mean Environment for DICOM Image Segmentation
5. A Review on the Techniques for Generation of Images using GAN
6. A Review of Techniques to Detect the GAN Generated Fake Images
7. Synthesis of Respiratory Signals using Conditional Generative Adversarial Networks from Scalogram Representation
8. Visual Similarity-Based Fashion Recommendation System
9. Deep learning based vegetation index estimation
10. Image Generation using Generative Adversarial Networks
11. Generative Adversarial Networks for Histopathology Staining
12. ANALYSIS OF FALSE DATA DETECTION RATE IN GENERATIVE ADVERSARIAL NETWORKS USING RECURRENT NEURAL NETWORK
13. WGGAN: A Wavelet-Guided Generative Adversarial Network for Thermal Image Translation
14. GENERATIVE ADVERSARIAL NETWORK FOR VIDEO ANALYTICS
15. Multimodal reconstruction of retinal images over unpaired datasets using cyclical generative adversarial networks
16. Generative Adversarial Network for Video Anomaly Detection
- No. of pages: 444
- Language: English
- Edition: 1
- Published: June 22, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128235195
- eBook ISBN: 9780128236130
AS
Arun Solanki
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.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, IndiaAN
Anand Nayyar
Dr. Anand Nayyar received his Ph.D (Computer Science) from Desh Bhagat University in 2017 in Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Faculty of Information Technology- Duy Tan University, Da Nang, Vietnam. He has published 100+ research papers in various high-impact journals. He has authored, co-authored, and edited 30+ books. He has 10 Australian patents and 1 Indian Design to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing. Awarded 30+ Awards for Teaching and Research, including Young Scientist, Best Scientist, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching. He is acting as Associate Editor for Wireless Networks (Springer), Computer Communications (Elsevier), IET-Quantum Communications, IET Wireless Sensor Systems, IET Networks, IJDST, IJISP, IJCINI. He is acting as Editor-in-Chief of IGI-Global, USA Journal titled “International Journal of Smart Vehicles and Smart Transportation (IJSVST)”.
Affiliations and expertise
Assistant Professor, Scientist, Vice-Chairman (Research), Director (IoT and Intelligent Systems Lab), School of Computer Science, Duy Tan University, Da Nang, Viet NamMN
Mohd Naved
Dr. Mohd Naved is a machine learning consultant and researcher currently teaching in Amity University, Noida, India, for various degree programs in Analytics and Machine Learning. He is actively engaged in academic research on various topics in management as well as on 21st century technologies. He has published more than 30 research papers in reputed journals. He has 16 patents in AI/ML and actively engages in commercialization of innovative products developed at university level. His interview has been published in various national and international magazines. A former data scientist, he is an alumnus of Delhi University. He holds a PhD from Noida International University.
Affiliations and expertise
Assistant Professor and HoD (Analytics and IB), Jagannath University; Various UG & PG programs, Analytics and Machine Learning Xcelerator Ninja, India