456 citations · 984 across the 34 of their papers we have counts for
4 papers · 1 filter
Estimating label quality and errors in semantic segmentation data via any model
Vedang Lad, Jonas Mueller
The labor-intensive annotation process of semantic segmentation datasets is often prone to errors, since humans struggle to label every pixel correctly. We study algorithms to auto…
Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors
Jesse Cummings, Elías Snorrason, Jonas Mueller
We present a straightforward statistical test to detect certain violations of the assumption that the data are Independent and Identically Distributed (IID). The specific form of v…
Detecting Errors in a Numerical Response via any Regression Model
Hang Zhou, Jonas Mueller, Mayank Kumar +2
Noise plagues many numerical datasets, where the recorded values in the data may fail to match the true underlying values due to reasons including: erroneous sensors, data entry/pr…
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators
Hui Wen Goh, Jonas Mueller
In real-world data labeling applications, annotators often provide imperfect labels. It is thus common to employ multiple annotators to label data with some overlap between their e…