The Machine Learning Center at Georgia Tech presents a seminar by Jan Ernst of Siemens Research. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 2:30 - 3:30 p.m. and is open to the public.
Automated Perception in the Real World: The Problem of Scarce Data
Machine perception is a key step toward artificial intelligence in domains such as self-driving cars, industrial automation, and robotics. Much progress has been made in the past decade, driven by machine learning, ever-increasing computational power, and the reliance on (seemingly) vast data sets. There are however critical issues in translating academic progress into the real world: available data sets may not match real-world environments well, and even if they are abundant and matching well, then interesting samples from a real-world perspective may be exceedingly rare and thus still be too sparsely represented to learn from directly. In this talk, I illustrate how we have approached this problem strategically as an example of industrial R&D from inception to product. I will also go in-depth on an approach to automatically infer previously unseen data by learning compositional visual concepts via mutual cycle consistency.
Jan Ernst is the Principal Scientist at Siemens Corporate Technology in Princeton, NJ. He received a Ph.D. degree fromUniversity of Erlangen-Nuremberg in Erlangen, Germany. He has 20 years of industrial R&D experience in the field of computer vision and machine learning. Before becoming Principal Scientist, Dr. Ernst has been in the positions of Director of Research Group, and Project Manager at Siemens. He is a certified R&D Project Management Professional.