456 citations · 984 across the 34 of their papers we have counts for
34 papers
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…
Identifying Incorrect Annotations in Multi-Label Classification Data
Aditya Thyagarajan, Elías Snorrason, Curtis Northcutt +1
In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes). Example applications include image (or docum…
Detecting Label Errors in Token Classification Data
Wei-Chen Wang, Jonas Mueller
Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consid…