Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras
Category
Author
Publication
Packt Publishing
Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.
Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and youll gain an understanding of the architecture and functioning of generative models through their practical implementation.
By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.
About the Author
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.
- Train a network on the 3D ShapeNet dataset to generate realistic shapes
- Generate anime characters using the Keras implementation of DCGAN
- Implement an SRGAN network to generate high-resolution images
- Train Age-cGAN on Wiki-Cropped images to improve face verification
- Use Conditional GANs for image-to-image translation
- Understand the generator and discriminator implementations of StackGAN in Keras
If youre a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.
- 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
