Generative adversarial networks for image-to-image translation /

"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 N...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Solanki, Arun, 1985- (Editor ), Nayyar, Anand (Editor ), Naved, Mohd (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2021.
Temas:
Machine learning.
Artificial intelligence.
Neural networks (Computer science)
Generative programming (Computer science)
Apprentissage automatique.
Intelligence artificielle.
R�eseaux neuronaux (Informatique)
Programmation g�en�erative.
artificial intelligence.
Artificial intelligence
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Generative Adversarial Networks for Image-to-Image Translation
  • Copyright
  • Contents
  • Contributors
  • Chapter 1: Super-resolution-based GAN for image processing: Recent advances and future trends
  • 1.1. Introduction
  • 1.1.1. Train the discriminator
  • 1.1.2. Train the generator
  • 1.1.3. Organization of the chapter
  • 1.2. Background study
  • 1.3. SR-GAN model for image processing
  • 1.3.1. Architecture of SR-GAN
  • 1.3.2. Network architecture
  • 1.3.3. Perceptual loss
  • 1.3.3.1. Content loss
  • 1.3.3.2. Adversarial loss
  • 1.4. Case study
  • 1.4.1. Case study 1: Application of EE-GAN to enhance object detection
  • 1.4.2. Case study 2: Edge-enhanced GAN for remote sensing image
  • 1.4.3. Case study 3: Application of SRGAN on video surveillance and forensic application
  • 1.4.4. Case study 4: Super-resolution of video using SRGAN
  • 1.5. Open issues and challenges
  • 1.6. Conclusion and future scope
  • References
  • Chapter 2: GAN models in natural language processing and image translation
  • 2.1. Introduction
  • 2.1.1. Variational auto encoders
  • 2.1.1.1. Drawback of VAE
  • 2.1.2. Brief introduction to GAN
  • 2.2. Basic GAN model classification based on learning
  • 2.2.1. Unsupervised learning
  • 2.2.1.1. Vanilla GAN
  • 2.2.1.2. WGAN
  • 2.2.1.3. WGAN-GP
  • 2.2.1.4. Info GAN
  • 2.2.1.5. BEGAN
  • 2.2.1.6. Unsupervised sequential GAN
  • 2.2.1.7. Parallel GAN
  • 2.2.1.8. Cycle GAN
  • 2.2.2. Semisupervised learning
  • 2.2.2.1. Semi GAN
  • 2.2.3. Supervised learning
  • 2.2.3.1. CGAN
  • 2.2.3.2. BiGAN
  • 2.2.3.3. ACGAN
  • 2.2.3.4. Supervised seq-GAN
  • 2.2.4. Comparison of GAN models
  • 2.2.5. Pros and cons of the GAN models
  • 2.3. GANs in natural language processing
  • 2.3.1. Application of GANs in natural language processing
  • 2.3.1.1. Generation of semantically similar human-understandable summaries using SeqGAN with policy gradient
  • Semantic similarity discriminator
  • 2.3.1.2. Generation of quality language descriptions and ranking using RankGAN
  • 2.3.1.3. Dialogue generation using reinforce GAN
  • 2.3.1.4. Text style transfer using UGAN
  • 2.3.1.5. Tibetan question-answer corpus generation using Qu-GAN
  • 2.3.1.6. Generation of the sentence with lexical constraints using BFGAN
  • 2.3.1.7. Short-spoken language intent classification with cSeq-GAN
  • 2.3.1.8. Recognition of Chinese characters using TH-GAN
  • 2.3.2. NLP datasets
  • 2.4. GANs in image generation and translation
  • 2.4.1. Applications of GANs in image generation and translation
  • 2.4.1.1. Ensemble learning GANs in face forensics
  • 2.4.1.2. Spherical image generation from the 2D sketch using SGANs
  • 2.4.1.3. Generation of radar images using TsGAN
  • 2.4.1.4. Generation of CT from MRI using MCRCGAN
  • 2.4.1.5. Generation of scenes from text using text-to-image GAN
  • 2.4.1.6. Gastritis image generation using PG-GAN
  • 2.4.1.7. Image-to-image translation using quality-aware GAN

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