Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs

2026-02-04

AI Summary

This work addresses the scarcity of high-quality annotations in perception tasks and the inherent aleatoric and epistemic uncertainties associated with human labeling. It proposes the first probabilistic label propagation method that maintains consistency even as the number of annotations approaches zero. Built upon the assumption of label smoothness in feature space, the approach leverages a probabilistic diffusion mechanism over a graph structure to propagate soft labels from a single annotation, thereby jointly modeling and quantifying both sources of uncertainty. The method substantially reduces the annotation budget required to achieve target performance, outperforms existing baselines on mainstream image datasets, and establishes a new state-of-the-art on the Data-Centric Image Classification benchmark.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Diffusion Models for Vision

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web

Abstract

Safe artificial intelligence for perception tasks remains a major challenge, partly due to the lack of data with high-quality labels. Annotations themselves are subject to aleatoric and epistemic uncertainty, which is typically ignored during annotation and evaluation. While crowdsourcing enables collecting multiple annotations per image to estimate these uncertainties, this approach is impractical at scale due to the required annotation effort. We introduce a probabilistic label spreading method that provides reliable estimates of aleatoric and epistemic uncertainty of labels. Assuming label smoothness over the feature space, we propagate single annotations using a graph-based diffusion method. We prove that label spreading yields consistent probability estimators even when the number of annotations per data point converges to zero. We present and analyze a scalable implementation of our method. Experimental results indicate that, compared to baselines, our approach substantially reduces the annotation budget required to achieve a desired label quality on common image datasets and achieves a new state of the art on the Data-Centric Image Classification benchmark.

Problem

• label uncertainty
• epistemic uncertainty
• aleatoric uncertainty
• annotation efficiency
• graph-based learning

Innovation

• probabilistic label spreading
• epistemic uncertainty
• graph-based diffusion
• label efficiency
• data-centric learning