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E-raamat: Generative Adversarial Networks for Image-to-Image Translation

Edited by , Edited by (Professor, Scientist, Vice-Chairman (Research) and Director at IoT and Intelligent Systems Lab, Duy Tan University,), Edited by (Assistant Professor, Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 22-Jun-2021
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128236130
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Generative Adversarial Networks for Image-to-Image TranslationSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 22-Jun-2021
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128236130
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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
Contributors xi
1 Super-resolution-based GAN for image processing: Recent advances and future trends
1(16)
Meenu Gupta
Meet Kumari
Rachna Jain
Lakshay
1.1 Introduction
1(3)
1.2 Background study
4(2)
1.3 SR-GAN model for image processing
6(3)
1.4 Case study
9(2)
1.5 Open issues and challenges
11(1)
1.6 Conclusion and future scope
12(5)
References
12(5)
2 GAN models in natural language processing and image translation
17(42)
E. Thirumagal
K. Saruladha
2.1 Introduction
17(1)
2.2 Basic GAN model classification based on learning
18(15)
2.3 GANs in natural language processing
33(9)
2.4 GANs in image generation and translation
42(8)
2.5 Evaluation metrics
50(2)
2.6 Tools and languages used for GAN research
52(2)
2.7 Open challenges for future research
54(1)
2.8 Conclusion
55(4)
References
55(4)
3 Generative adversarial networks and their variants
59(22)
Er. Aarti
3.1 Introduction of generative adversarial network (GAN)
59(5)
3.2 Related work
64(1)
3.3 Deep-learning methods
65(3)
3.4 Variants of GAN
68(5)
3.5 Applications of GAN
73(4)
3.6 Conclusion
77(4)
References
77(4)
4 Comparative analysis of filtering methods in fuzzy C-means: Environment for DICOM image segmentation
81(18)
D. Nagarajan
Kavikumar Jacob
Aida Mustapha
Udaya Mouni Boppana
Najihah Chaini
4.1 Introduction
81(3)
4.2 Related works
84(3)
4.3 Methodology
87(3)
4.4 Experimental analysis
90(2)
4.5 Performance analysis
92(1)
4.6 Results and discussion
93(3)
4.7 Conclusion
96(3)
Acknowledgment
96(1)
References
96(3)
5 A review of the techniques of images using GAN
99(26)
Rituraj Soni
Tanvi Arora
5.1 Introduction to GANs
99(3)
5.2 GAN architectures
102(15)
5.3 Discussion on research gaps
117(2)
5.4 GAN applications
119(1)
5.5 Conclusion
120(5)
References
121(4)
6 A review of techniques to detect the GAN-generated fake images
125(36)
Tanvi Arora
Rituraj Soni
6.1 Introduction
125(3)
6.2 DeepFake
128(3)
6.3 DeepFake challenges
131(1)
6.4 GAN-based techniques for generating DeepFake
132(10)
6.5 Artificial intelligence-based methods to detect DeepFakes
142(4)
6.6 Comparative study of artificial intelligence-based techniques to detect the face manipulation in GAN-generated fake images
146(7)
6.7 Legal and ethical considerations
153(1)
6.8 Conclusion and future scope
154(7)
References
156(5)
7 Synthesis of respiratory signals using conditional generative adversarial networks from scalogram representation
161(24)
S. Jayalakshmy
Lakshmi Priya
Gnanou Florence Sudha
7.1 Introduction
161(2)
7.2 Related work
163(3)
7.3 GAN for signal synthesis
166(8)
7.4 Results and discussion
174(7)
7.5 Conclusion and future scope
181(4)
References
182(3)
8 Visual similarity-based fashion recommendation system
185(20)
Betul Ay
Galip Aydin
8.1 Introduction
185(2)
8.2 Related works
187(4)
8.3 Fashion recommendation system
191(5)
8.4 Experiments and results
196(4)
8.5 Conclusion and future works
200(5)
References
202(3)
9 Deep learning-based vegetation index estimation
205(2)
Patricia L. Suarez
Angel D. Sappa
Boris X. Vintimilla
9.1 Introduction
205(2)
92 Related work
207(28)
9.3 Proposed approach
212(8)
9.4 Results and discussions
220(12)
9.5 Conclusions
232(3)
Acknowledgments
232(1)
References
232(3)
10 Image generation using generative adversarial networks
235(28)
Omkar Metri
H.R. Mamatha
10.1 Introduction to deep learning
235(4)
10.2 Introduction to GAN
239(5)
10.3 Applications
244(16)
10.4 Future of GANs
260(3)
References
260(3)
11 Generative adversarial networks for histopathology staining
263(26)
Aashutosh Ganesh
Koshy George
11.1 Introduction
263(3)
11.2 Generative adversarial networks
266(3)
11.3 The image-to-image translational problem
269(2)
11.4 Histology and medical imaging
271(1)
11.5 Network architecture and dataset
272(3)
11.6 Results and discussions
275(6)
11.7 Conclusions
281(8)
Appendix: Network architectures
281(2)
References
283(6)
12 Analysis of false data detection rate in generative adversarial networks using recurrent neural network
289(24)
A. Sampath Kumar
Leta Tesfaye Jule
Krishnaraj Ramaswamy
S. Sountharrajan
N. Yuuvaraj
Amir H. Gandomi
12.1 Introduction
289(2)
12.2 Related works
291(2)
12.3 Methods
293(1)
12.4 GAN-RNN architecture
294(3)
12.5 Performance evaluation
297(11)
12.6 Conclusions
308(5)
References
310(3)
13 WGGAN: A wavelet-guided generative adversarial network for thermal image translation
313(16)
Ran Zhang
Junchi Bin
Zheng Liu
Erik Blasch
13.1 Introduction
313(2)
13.2 Related work
315(1)
13.3 Wavelet-guided generative adversarial network
316(5)
13.4 Experiments
321(4)
13.5 Conclusion
325(4)
Acknowledgment
326(1)
References
326(3)
14 Generative adversarial network for video analytics
329(18)
A. Sasithradevi
S. Mohamed Mansoor Roomi
R. Sivaranjani
14.1 Introduction
329(2)
14.2 Building blocks of GAN
331(2)
14.3 GAN variations for video analytics
333(9)
14.4 Discussion
342(1)
14.5 Conclusion
343(4)
References
344(3)
15 Multimodal reconstruction of retinal images over unpaired datasets using cyclical generative adversarial networks
347(30)
Alvaro S. Hervella
Jose Rouco
Jorge Novo
Marcos Ortega
15.1 Introduction
347(2)
15.2 Related research
349(2)
15.3 Multimodal reconstruction of retinal images
351(9)
15.4 Experiments and results
360(11)
15.5 Discussion and conclusions
371(6)
Acknowledgments
373(1)
References
374(3)
16 Generative adversarial network for video anomaly detection
377(40)
Thittaporn Ganokratanaa
Supavadee Aramvith
16.1 Introduction
377(4)
16.2 Literature review
381(8)
16.3 Training a generative adversarial network
389(13)
16.4 Experimental results
402(15)
16.5 Summary
417(1)
References 417(4)
Index 421
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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