Abstract: Crowd-sourced steering does not sound as appealing as automated driving. We need to go beyond supervised learning for automated driving, including for computer vision problems seeing great progress with strong supervision today. First, we will motivate exciting scientific problems that have huge implications in the research and development of long-term large-scale autonomous robots, such as unsupervised domain adaptation, self-supervised learning, and robustness to edge cases. Second, we will discuss recent state-of-the-art results obtained in the ML team at TRI for unsupervised domain adaptation from simulation and self-supervised depth and pose prediction from monocular imagery.

Speaker: Adrien Gaidon (@adnothing),, Machine Intelligence Lead and senior research scientist at the Toyota Research Institute (TRI) in Los Altos, CA, USA. He’s working on open problems in world-scale learning for autonomous driving. Adrien received his PhD from Microsoft Research - Inria Paris in 2012 and has over a decade of experience in academic and industrial computer vision, with over 30 publications, top entries in international computer vision competitions, multiple best reviewer awards, international press coverage for his work on deep learning with simulation, and was a guest editor for the International Journal of Computer Vision. Prior to joining Toyota Adrien was a member of our Computer Vision  research team in Grenoble.

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