Training Data for Your CNN: What You Need and How to Get It

Tuesday, May 21, 1:00 PM - 1:30 PM
Summit Track: 
Room 203/204

A fundamental building block for AI development is the development of a proper training set to allow effective training of neural nets. Developing such a training set constitutes a major challenge, requiring multi-disciplinary knowledge spanning data science, computer vision, machine learning and project management. This talk will provide an outline of common workflows for developing training sets for AI applications, touching on how to start, how to leverage existing tools and labeling companies and how to assess whether the developed database is sufficiently comprehensive and capable of effectively sustaining AI algorithm development for computer vision applications.


Carlo Dal Mutto

CTO, Aquifi

Carlo Dal Mutto is a computer vision and machine learning engineer interested in the application of deep learning techniques to 3D data. He received a Ph.D. (Dottorato di ricerca) in Information Engineering from the University of Padova, Italy in 2012. Currently he is CTO at Aquifi, focused on delivering 3D-AI solutions for logistics and manufacturing. He is inventor of several patents, has been invited speaker at major technical conferences, and has co-authored research papers, two book chapters and two books on 3D data acquisition and processing. He has served as a reviewer and TPC member for CVPR, ECCV, ICCV, 3DPVT, 3DIMPVT, ICME, IJCV, IEEE SPL, and Springer MVAP.

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