
Generative Adversarial Networks for Image-to-Image Translation
- 1st Edition - June 23, 2021
- Editors: Arun Solanki, Anand Nayyar, Mohd Naved
- Language: English
- 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 Ad… Read more
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- 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
- Edition: 1
- Published: June 23, 2021
- Language: English
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Arun Solanki
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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, Vietnam. He has published numerous research papers in various high-impact journals and holds 10 Australian patents and 1 Indian Design to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing.
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Mohd Naved
Dr. Mohd Naved is an Associate Professor at Jaipuria Institute of Management in Noida, India, with over a decade of experience in Business Analytics, Data Science, and Artificial Intelligence. His research focuses on the applications of business analytics, data science, and artificial intelligence across various industries.