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ECE Seminar or Event

Architecting Deep Convolutional Neural Networks for Computer Vision

Michael Maire


Research Assistant Professor
Toyota Technological Institute at Chicago
 
Monday, March 05, 2018
09:00am - 10:00am
3427 EECS

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About the Event

Deep convolutional neural networks (CNNs) are central to modern computer vision systems. In this talk, I will present recent work exploring ideas about both CNN architectures and training procedures. I will connect novel architectural design principles with specific capabilities exhibited by networks implementing them. Of particular importance is a multigrid extension of CNNs, in which network layers operate across scale space. Multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish visual tasks, which standard CNNs fail. On the training side, I will discuss pathways for scaling visual learning beyond current supervised approaches. Self-supervision, by deriving informative tasks from unlabeled data, provides one such route. In addition to our work in this area, I will also discuss a new regularization technique that squeezes more information from detailed and structured labels when training CNNs in a supervised fashion.

Biography

Michael Maire is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC). He received a B.S. with honors from the California Institute of Technology (Caltech) in 2003, and a Ph.D. in computer science from the University of California, Berkeley, in 2009. Prior to joining TTIC, he was a postdoctoral scholar in the Department of Electrical Engineering at Caltech.

Additional Information

Contact: Linda Scovel

Email: lscovel@umich.edu

Sponsor(s): ECE

Open to: UM Only