Online Semi-Supervised Learning and Face Recognition
This project focuses on real-time learning without explicit feedback.
This work combines the ideas of semi-supervised learning on
approximate graphs and online learning. In particular, we develop
algorithms that iteratively build a graphical representation of
the world and update it on-the-fly with observed examples
(both labeled and unlabeled). We proved regret bounds of the solutions,
demonstrated that the system can recognize faces in real-time
even in a resource constraint environment and can take advantage
of the manifold structure to outperform existing methods.
The following videos show how online semi-supervised
learning can be used to train a robust face recognizer
of a person from just a single frontal image:
Online Semi-Supervised Learning on Quantized GraphsMichal Valko, Branislav Kveton, Daniel Ting, Ling Huang. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI) , July 2010. [pdf]
Online Semi-Supervised Perception: Real-Time Learning without Explicit FeedbackBranislav Kveton, Michal Valko, Matthai Philipose, Ling Huang. In Proceedings of the 4th IEEE Online Learning for Computer Vision Workshop (OLCV) , 2010. [pdf] Awarded best paper!
Semi-Supervised Learning with Max-Margin Graph CutsBranislav Kveton, Michal Valko, Ali Rahimi, Ling Huang. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), May 2010. [pdf]