Self-Supervised Visual Learning and Synthesis

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.
Can one discover useful visual representations without the use of
explicitly curated labels? In this talk, I will present several case
studies exploring the paradigm of self-supervised learning — using
raw data as its own supervision. Several ways of defining objective
functions in high-dimensional spaces will be discussed, including the
use of General Adversarial Networks (GANs) to learn the objective
function directly from the data. Applications in image synthesis will
be shown, including automatic colorization, novel view synthesis,
image-to-image translation, and, terrifyingly, #edges2cats.
Alexei (Alyosha) Efros joined UC Berkeley in 2013 as associate professor of Electrical Engineering and Computer Science. Prior to that, he was nine years on the faculty of Carnegie Mellon University, and has also been affiliated with ‰cole 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. Alyosha received his PhD in 2003 from UC Berkeley. He is a recipient of CVPR Best Paper Award (2006), NSF CAREER award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), Finmeccanica Career Development Chair (2010), SIGGRAPH Significant New Researcher Award (2010), ECCV Best Paper Honorable Mention (2010), and the Helmholtz Test-of-Time Prize (2013).