Meny
 

Generative Adversarial Networks for Image-to-Image Translation

Arun Solanki (Redaktør) ; Anand Nayyar (Redaktør) ; Mohd Naved (Redaktør)

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. Les mer
Vår pris
1769,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 7 virkedager

Paperback
Legg i
Paperback
Legg i
Vår pris: 1769,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 7 virkedager

Om boka

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.

Fakta

Innholdsfortegnelse

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

Om forfatteren

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. Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Faculty of Information Technology- Duy Tan University, Da Nang, Vietnam. A Certified Professional with 75+ Professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published 100+ Research Papers in various National International Journals (Scopus/SCI/SCIE/SSCI Indexed) with High Impact Factor. Published 50+ Papers in International Conferences indexed with Springer, IEEE Xplore and ACM Digital Library. Member of more than 50+ Associations as Senior and Life Member including IEEE, ACM. He has authored/co-authored cum Edited 30+ Books of Computer Science. Associated with more than 500+ International Conferences as Programme Committee/Chair/Advisory Board/Review Board member. He has 10 Australian Patent and 1 Indian Design to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing. He is currently working in the area of Wireless Sensor Networks, IoT, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Drones, Blockchain, Cyber Security, Network Simulation and Wireless Communications. Awarded 30+ Awards for Teaching and Research-Young Scientist, Best Scientist, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching and many more. 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)". 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