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.
Show moreGenerative 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.
Show more1. 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
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, 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.
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.
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