Generative Adversarial Networks

4 hours of instruction

This course covers the area in image analysis and computer vision that deals with generative models. By the end of this course students will be able to implement a GAN model to generate new images from a set of training examples.

OBJECTIVES

1. Summarize the basis of generative adversarial networks and its applications
2. Define a combined model and generate images using the created GAN model

PREREQUISITES

Advanced CNN

SYLLABUS & TOPICS COVERED

1. GAN Overview
   • Generative modeling and its use cases in computer vision and image processing
   • Summary the basis of generative adversarial networks
2. GAN Implementation
   • Definition of discriminator and generator models
   • Implementation of the training process for GANs
   • Generation image samples for modeling

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.