Finding Label and Model Errors in Perception Data With Learned Observation Assertions

TL;DR

This paper introduces Fixy, a system that learns to identify errors in labeled perception data by modeling feature distributions, significantly improving error detection precision in autonomous vehicle datasets.

Contribution

The paper presents a novel abstraction called learned observation assertions and implements it in Fixy, which leverages existing data and models to detect label errors more accurately.

Findings

Fixy achieves up to 2× higher precision in error ranking.

Fixy effectively utilizes existing resources to learn feature distributions.

The approach outperforms recent model assertion techniques and uncertainty sampling.

Abstract

ML is being deployed in complex, real-world scenarios where errors have impactful consequences. In these systems, thorough testing of the ML pipelines is critical. A key component in ML deployment pipelines is the curation of labeled training data. Common practice in the ML literature assumes that labels are the ground truth. However, in our experience in a large autonomous vehicle development center, we have found that vendors can often provide erroneous labels, which can lead to downstream safety risks in trained models. To address these issues, we propose a new abstraction, learned observation assertions, and implement it in a system called Fixy. Fixy leverages existing organizational resources, such as existing (possibly noisy) labeled datasets or previously trained ML models, to learn a probabilistic model for finding errors in human- or model-generated labels. Given user-provided features and these existing resources, Fixy learns feature distributions that specify likely and unlikely values (e.g., that a speed of 30mph is likely but 300mph is unlikely). It then uses these feature distributions to score labels for potential errors. We show that FIxy can automatically rank potential errors in real datasets with up to 2$\times$ higher precision compared to recent work on model assertions and standard techniques such as uncertainty sampling.