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

arXiv: https://arxiv.org/abs/2604.27562 · HTML: https://mlsanity.com/paper/2604.27562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Semi-supervised learning with max-margin graph cuts](https://mlsanity.com/paper/2604.26818/) (2026)

## Built on by (most cited first, 1 of 1)

- [Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning](https://mlsanity.com/paper/2605.03495/) (2026)

## Abstract (arXiv)

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.