Modern Machine Learning is hitting the limits of purely supervised learning. Hence, self-supervised learning is emerging as a promising alternative, or at least a complementary approach. In this talk, we will discuss how computer vision is pushing beyond the limits of supervised learning using self-supervised analysis-by-synthesis, i.e. model based reconstruction. In particular, we will highlight recent progress at the confluence of neural approaches and simulation to ensure the scalability and robustness of deep neural networks for 3D vision.
Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research - Inria Paris in 2012, has over 50 publications and patents in ML & Computer Vision (cf. Google Scholar), and his research is used in a variety of domains, including automated driving