Online semi-supervised perception: Real-time learning without explicit feedback
Abstract
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets.
Cite
@article{arxiv.2604.27562,
title = {Online semi-supervised perception: Real-time learning without explicit feedback},
author = {Branislav Kveton and Michal Valko and Matthai Phillipose and Ling Huang},
journal= {arXiv preprint arXiv:2604.27562},
year = {2026}
}
Comments
IEEE Computer Vision and Pattern Recognition Workshop on Online Learning for Computer Vision (CVPR 2010 OLCV)