Abstract: GANs are one of the most useful DL techniques in recent years, particularly for the tasks of data synthesis. Estimating an intrinsic data distribution from a given dataset and generating newer data that look like one from the given dataset is one of the significant successes of GANs. In this talk, I will present the fundamental principle behind the GANs, a mathematical formulation of it, how to build GANs using TensorFlow APIs, and how to train them to generate new images. I will also cover some of the extended GANs architecture and provide a future roadmap for beginners and advanced users of the GANs framework. This talk will have two parts - a theory class followed by a half-day of tutorials where I will show baseline code to build GANs models and train them; it will be followed by inference of image synthesis.
Bio: Ajay K Baranwal is the Center Director at CDLe (Center for Deep Learning in Electronics Manufacturing). He leads applied data science research and development efforts to solve electronics and semiconductor manufacturing problems. Many of his work at the Center relates to machine vision, learning from limited data, and building digital twins to synthesize new data. Before the Center, he has worked on several TensorFlow-based applications, including a Prediction and Diagnostic system, a Document retrieval, and an information extraction system. He holds multiple patents, is coauthor of industrial papers and has been a speaker at related conferences. He is also a co-author of a book named “What’s new in TensorFlow 2.0."