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TERATEC 2018 Forum
Workshop 7 - Wednesday, June 20 from 14h00 to 17h30

Deep Learning by doing

Using generated images to better train Deep Learning models
Pierre NOUBEL, Business Development Manager, OKTAL Synthetic Environment

Abstract : Training Convolutional Neural Networks lies at the heart of Deep Learning. The problem is that for that training to be efficient, you need very large data sets, with millions of different images in different contexts.
OKTAL-SE used its experience with synthetic environments and its tool SE-Workbench to generate large amounts of simulated images with metadata allowing to enhance object recognition on satellite images.
By varying several parameters such as satellite position or spectral response for example, OKTAL-SE was able to use data sets with more consistency in all possible to context, and at the end get better results during inference.

Biography : Pierre Noubel has always had a passion for science and new technologies. His background, mostly spent abroad, naturally revolved around new technologies, IT and engineering sciences.
He now works at OKTAL Synthetic Environment where new solutions have reecently been developed, aimed at Deep Learning.
OKTAL-SE purpose is to provide customers with state of the art multi-sensor simulation software and services. Thanks to the reliability and the performance of our modeling and simulation COTS products, OKTAL-SE has become an international reference in terms of synthetic environment rendering software in both Electro-Optic (Visible, Infrared) and Radio-Frequency (Radar, GNSS) domains, with clients from both industry and public bodies (DGA, Airbus, Dassault, Safran, etc.) in France and abroad.

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