Watch the talk by Alexei Efros entitled Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder that took place at CIIRC.
The lecture was organized by the IMPACT project
The whole video available here
Abstract: 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.
Bio: 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).