Online semi-supervised perception: Real-time learning without explicit feedback

arXiv cs.LG / 5/1/2026

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Key Points

The paper presents a real-time perception learning algorithm that updates its model using unlabeled online data without requiring explicit feedback signals.
It merges semi-supervised learning on graphs with online learning by iteratively building and refining a graph-based representation of the environment.
Labeled data are used only offline to set an initial bias, while a continuous stream of unlabeled examples is used online to adapt and improve performance.
The authors provide implementation guidance, establish a regret bound guaranteeing solution quality, and demonstrate the approach on real-time face recognition.
In experiments on three challenging video datasets, the proposed real-time face recognizer achieves better precision and recall than prior approaches.

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.