Ahirwar / Unknown Generative Adversarial Networks Projects

Build next-generation generative models using TensorFlow and Keras
1. Auflage 2019
ISBN: 978-1-78913-419-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 316 Seiten

No detailed description available for "Generative Adversarial Networks Projects".

Autoren/Hrsg.

Ahirwar, Kailash

Unknown,

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Table of Contents - Introduction to Generative Adversarial Networks
- 3D-GAN - Generating Shapes Using GANs
- Face Aging Using Conditional GAN
- Generating Anime Characters Using DCGANs
- Using SRGANs to Generate Photo-Realistic Images
- StackGAN- Text to Photo-Realistic Image Synthesis
- CycleGAN- Turn Paintings into Photos
- Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks
- Predicting the Future of GANs

Über Autor(innen)

Ahirwar Kailash:

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.