3 citations · 3 across the 5 of their papers we have counts for
5 papers
Label Errors in the Tobacco3482 Dataset
Gordon Lim, Stefan Larson, Kevin Leach
Tobacco3482 is a widely used document classification benchmark dataset. However, our manual inspection of the entire dataset uncovers widespread ontological issues, especially larg…
Towards Fair Pay and Equal Work: Imposing View Time Limits in Crowdsourced Image Classification
Gordon Lim, Stefan Larson, Yu Huang +1
Crowdsourcing is a common approach to rapidly annotate large volumes of data in machine learning applications. Typically, crowd workers are compensated with a flat rate based on an…
Robust Testing for Deep Learning using Human Label Noise
Gordon Lim, Stefan Larson, Kevin Leach
In deep learning (DL) systems, label noise in training datasets often degrades model performance, as models may learn incorrect patterns from mislabeled data. The area of Learning…
On Evaluation of Document Classification using RVL-CDIP
Stefan Larson, Gordon Lim, Kevin Leach
The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of…
Evaluating Out-of-Distribution Performance on Document Image Classifiers
Stefan Larson, Gordon Lim, Yutong Ai +2
The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizabil…