Building Efficient CNN Models for Mobile and Embedded Applications

Wednesday, May 23, 2:10 PM - 2:40 PM
Summit Track: 
Technical Insights I
Location: 
Mission City Ballroom B2-B5

Recent advances in efficient deep learning models have led to many potential applications in mobile and embedded devices. In this talk, I will discuss state-of-the-art model architectures, and introduce our work on real-time style transfer and pose estimation on mobile phones.

Speaker(s):

Peter Vajda

Research Scientist, Facebook

Peter Vajda is a Research Scientist working on computer vision at Facebook since 2014. Before joining Facebook, he was a Visiting Assistant Professor in Professor Bernd Girod’s group in Stanford University, Stanford, USA. He was working on personalized multimedia system and mobile visual search. Peter received a M.Sc. in Computer Science from the Vrije Universiteit, Amsterdam, Netherlands and a M.Sc. in Program Designer Mathematician from Eötvös Loránd University, Budapest, Hungary. Peter completed his Ph.D. with Prof. Touradj Ebrahimi at the Ecole Polytechnique Fédéral de Lausanne (EPFL), Lausanne, Switzerland, 2012.

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