Early Bird Ends in:
Jakub Langr graduated from the University of Oxford where he also taught at OU Computing Services. He has worked in data science since 2013, most recently as a Data Science Tech Lead at Filtered.com and as an R&D Data Scientist at Mudano. Jakub is a co-author of GANs in Action by Manning Publications. Jakub also designed and teaches Data Science courses at the University of Birmingham, numerous private companies and is a guest lecturer at the University of Oxford.
Overview of Generative Adversarial Networks (GANs)
Until recently, generative modeling of any kind has had limited success. But now that Generative Adversarial Networks (GANs) have recently reached few tremendous milestones (and truly exponential growth in the interest in this technology), we are now closer to a general purpose framework for generating new data.
Now GANs can achieve a variety of applications such as synthesizing full-HD synthetic faces, to semi-supervised learning as well as defending and mastering adversarial examples, we can discuss them in this talk. In this talk, we will start with the basics of generative models, but eventually, explore the state of the art in generating full HD images as presented in https://arxiv.org/abs/1710.10196 and dive into adversarial attacks and why this matters to all computer vision algorithms.
GANs are a novel approach to generating new data or on a variety of adjacent problems that leverages the power of deep learning and two competing agents to achieve breath-taking results.