Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza
Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn 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)
14
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15
Index

Cross-Domain GANs

In computer vision, computer graphics, and image processing, a number of tasks involve translating an image from one form to another. The 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...