Unsupervised Learning in Computer Vision
Computer vision has made great progress through the use of deep learning, trained with large-scale labeled data. However, good labeled data requires expertise and curation and can be 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 -- for tasks in computer vision and computer graphics.
Alexei Efros (associate professor, UC Berkeley) works 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. He is a recipient of NSF CAREER award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), SIGGRAPH Significant New Researcher Award (2010), and the Helmholtz Test-of-Time Prize (2013).