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Generative Adversarial Networks for Image-to-Image Translation

Solanki, Arun Nayyar, Anand Naved, Mohd
Heftet / 2021 / Engelsk

Produktdetaljer

ISBN
9780128235195
Publisert
2021-06-23
Utgiver
Vendor
Academic Press Inc
Vekt
450 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
444

Redaktør
Solanki, Arun
Nayyar, Anand
Naved, Mohd

Om bidragsyterne

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 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)”. Dr. Mohd Naved is an Associate Professor in Jaipuria Institute of Management, Noida, India. He has an impressive career spanning over a decade in the fields of Business Analytics, Data Science, and Artificial Intelligence. As an educator, Dr. Naved has consistently demonstrated a commitment to the highest standards of teaching and mentoring, ensuring that his students receive an education that is both cutting-edge and grounded in real-world experience. His dedication to helping students achieve their full potential extends beyond the classroom, as he has been an active participant in the university’s Mentor-Mentee Program, providing guidance and support to over 150 undergraduate and postgraduate students. In addition to his teaching prowess, Dr. Naved has excelled in the areas of education management, research, and curriculum development. He has served on various committees and led initiatives related to curriculum development, faculty recruitment and retention, and accreditation, contributing to the institutions he has worked with becoming centers of academic excellence in their respective fields. He has also successfully led the launch of several BBA/MBA programs, resulting in increased admissions and student satisfaction. As a researcher, Dr. Naved has made significant contributions to the fields of Business Analytics, Data Science, and Artificial Intelligence, with over 80+ publications in reputed scholarly journals and books. His research focuses on the applications of these disciplines in various industries, and he has supervised numerous research projects and dissertations, guiding students to successful outcomes.

Generative Adversarial Networks for Image-to-Image Translation

Solanki, Arun Nayyar, Anand Naved, Mohd
Heftet / 2021 / Engelsk
Solanki, Arun Nayyar, Anand Naved, Mohd
Heftet / 2021 / Engelsk
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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.
Les mer
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
Les mer
Examines the concepts and technical methods of data deduplication for a wide variety of applications
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
Les mer

Relaterte produkter

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.
Les mer
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
Les mer
Examines the concepts and technical methods of data deduplication for a wide variety of applications
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
Les mer

Produktdetaljer

ISBN
9780128235195
Publisert
2021-06-23
Utgiver
Vendor
Academic Press Inc
Vekt
450 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
444

Redaktør
Solanki, Arun
Nayyar, Anand
Naved, Mohd

Om bidragsyterne

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 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)”. Dr. Mohd Naved is an Associate Professor in Jaipuria Institute of Management, Noida, India. He has an impressive career spanning over a decade in the fields of Business Analytics, Data Science, and Artificial Intelligence. As an educator, Dr. Naved has consistently demonstrated a commitment to the highest standards of teaching and mentoring, ensuring that his students receive an education that is both cutting-edge and grounded in real-world experience. His dedication to helping students achieve their full potential extends beyond the classroom, as he has been an active participant in the university’s Mentor-Mentee Program, providing guidance and support to over 150 undergraduate and postgraduate students. In addition to his teaching prowess, Dr. Naved has excelled in the areas of education management, research, and curriculum development. He has served on various committees and led initiatives related to curriculum development, faculty recruitment and retention, and accreditation, contributing to the institutions he has worked with becoming centers of academic excellence in their respective fields. He has also successfully led the launch of several BBA/MBA programs, resulting in increased admissions and student satisfaction. As a researcher, Dr. Naved has made significant contributions to the fields of Business Analytics, Data Science, and Artificial Intelligence, with over 80+ publications in reputed scholarly journals and books. His research focuses on the applications of these disciplines in various industries, and he has supervised numerous research projects and dissertations, guiding students to successful outcomes.

Relaterte produkter