Deep learning has profoundly changed the field of computer vision in the last few years. Many computer vision problems have been recast with techniques from deep learning and in turn achieved state of the art results and become industry standards. Over the course of the lecture I will provide an overview about the central ideas of deep learning as applied to computer vision. In the course of the lecture I will build on these ideas to describe the current state of research in artificial vision focusing on the topics of image recognition, object localization and image synthesis. The goal of these lectures will be to teach the core ideas, provide a high level overview of how deep learning has influenced computer vision and finally provide a series of hands-on tools so that students may get started in applying these ideas.
NB! THIS EVENT WILL NOT BE RECORDED. YOU HAVE TO COME TO THE LECTURE IN PERSON!
Bio: Jonathon Shlens received his Ph.D in computational neuroscience from UC San Diego in 2007 where his research focused on applying machine learning towards understanding visual processing in real biological systems. He was previously a research fellow at the Howard Hughes Medical Institute, a research engineer at Pixar Animation Studios and a Miller Fellow at UC Berkeley. He has been at Google Research since 2010 and is currently a research scientist focused on building scalable vision systems. During his time at Google, he has been a core contributor to deep learning systems including the recently open-sourced TensorFlow. His research interests have spanned the development of state-of-the-art image recognition systems and training algorithms for deep networks.
Sponsor: Albert de Roeck