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Prof. Alexei Efros
Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder
On 2018-05-25 11:00
"Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder"

Prof. Alexei Efros 

(UC Berkeley, recipient of ACM Prize in Computing 2016)
Friday 25.5.2018 at 11:00
CIIRC Seminar Room A-1001 (Building A, 10th floor)
Computer vision has made impressive gains through the use of deep learning
models, trained with large-scale labeled data. However, labels require expertise
and curation and are expensive to collect. Even worse, direct semantic
supervision often leads the learning  algorithms “cheating” and taking
shortcuts, instead of actually doing the work. In this talk, I will briefly
summarize several of my group’s efforts to combat this using self-supervision,
meta-supervision, and curiosity — all ways of using the data as its  own
supervision. These lead to practical applications in image synthesis (such as
pix2pix and cycleGAN), image forensics, audio-visual source separation, etc.
Alexei Efros is a professor of Electrical Engineering and Computer Sciences at
UC Berkeley. Before 2013, he was nine years on the faculty of Carnegie Mellon
University, and has also been affiliated with École Normale Supérieure/INRIA
and University of Oxford.  His research is in the area of computer vision and
computer graphics, especially at the intersection of the two. He is particularly
interested in using data-driven techniques to tackle problems where large
quantities of unlabeled visual data are readily available.  Efros received his
PhD in 2003 from UC Berkeley. He is a recipient of the Sloan Fellowship (2008),
Guggenheim Fellowship (2008), SIGGRAPH Significant New Researcher Award (2010),
3 Helmholtz Test-of-Time Prizes (1999, 2003, 2005), and the ACM Prize in
Computing  (2016).  
For more information on IMPACT seminars, please see
Josef Sivic and Tomas Pajdla,
IMPACT Project (
Czech Institute of Robotics, Informatics and Cybernetics (
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