Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (16 chapters)
Advanced Deep Learning with Keras
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Chapter 7. Cross-Domain GANs

In computer vision, computer graphics, and image processing a number of tasks involve translating an image from one form to another. As an example, colorization of grayscale images, converting satellite images to maps, changing the artwork style of one artist to another, making night-time images into daytime, and summer photos to winter, are just a few examples. These tasks are referred to as cross-domain transfer and will be the focus of this chapter. An image in the source domain is transferred to a target domain resulting in a new translated image.

A cross-domain transfer has a number of practical applications in the real world. As an example, in autonomous driving research, collecting road scene driving data is both time-consuming and expensive. In order to cover as many scene variations as possible in that example, the roads would be traversed during different weather conditions, seasons, and times giving us a large and varied amount of data. With the use...