Book Image

Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Book Image

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Table of Contents (11 chapters)

Pix2pix - Image-to-Image translation GAN

This network uses a conditional generative adversarial network (cGAN) to learn mapping from the input and output of an image. Some of the examples that can be done from the original paper are as follows:

 Pix2pix examples of cGANs 

In the handbags example, the network learns how to color a black and white image. Here, the training dataset has the input image in black and white and the target image is the color version.


CycleGAN is also an image-to-image translator but without input/output pairs. For example, to generate photos from paintings, convert a horse image into a zebra image:


In a discriminator network, use of dropout is important. Otherwise, it may produce a poor result.

The generator network takes random noise as input and produces a realistic image as output. Running a generator network for different kinds of random noise produces different types of realistic images. The second network, which is known as the discriminator network...