Book Image

Generative Adversarial Networks Projects

By : Kailash Ahirwar
Book Image

Generative Adversarial Networks Projects

By: Kailash Ahirwar

Overview of this book

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 you’ll 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.
Table of Contents (11 chapters)

Generating Anime Characters Using DCGANs

As we know, convolution layers are really good at processing images. They are capable of learning important features, such as edges, shapes, and complex objects, effectively, as shown in neural networks, such as Inception, AlexNet, Visual Geometry Group (VGG), and ResNet. Ian Goodfellow and others proposed a Generative Adversarial Network (GAN) with dense layers in their paper titled Generative Adversarial Nets, which can be found at the following link: https://arxiv.org/pdf/1406.2661.pdf. Complex neural networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) were not initially tested in GANs. The development of Deep Convolutional Generative Adversarial Networks (DCGANs) was an important step toward using CNNs for image generation. A DCGAN uses convolutional layers instead...