Label Error Detection
1개 벤치마크 · 논문 21편 · 이 태스크의 논문 보기 →
Benchmarks
TREC-6
Most implemented
Identifying Incorrect Annotations in Multi-Label Classification Data
CleanPatrick: A Benchmark for Image Data Cleaning
AQuA: A Benchmarking Tool for Label Quality Assessment
Identifying Label Errors in Object Detection Datasets by Loss Inspection
The Re-Label Method For Data-Centric Machine Learning
Papers
Data filtering methods for training language models
Data quality is a critical factor in the effectiveness of machine learning models. Label errors, present even in widely used benchmarks, introduce noise into training data and reduce model generalization. In this work, w…
Linguistic AcceptabilityEmotion ClassificationLabel Error DetectionText ClassificationDetecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models
Deep learning-based medical image segmentation typically relies on ground truth (GT) labels obtained through manual annotation, but these can be prone to random errors or systematic biases. This study examines the robust…
Medical Image SegmentationLabel Error DetectionA Human-in-the-Loop Label Error Detection Framework Applied to Arabic-Script HTR Datasets
Despite recent advances, Handwritten Text Recognition (HTR) for Arabic-script languages still lags behind Latin-script HTR. Part of the problem is dataset quality. To help closing this gap, we propose a two-stage framewo…
Handwritten Text RecognitionLabel Error DetectionAdaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection
Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As machine learning becomes more widesprea…
Label Error DetectionHard Samples, Bad Labels: Robust Loss Functions That Know When to Back Off
Incorrectly labelled training data are frustratingly ubiquitous in both benchmark and specially curated datasets. Such mislabelling clearly adversely affects the performance and generalizability of models trained through…
Label Error DetectionTowards Cross-Modal Error Detection with Tables and Images
Ensuring data quality at scale remains a persistent challenge for large organizations. Despite recent advances, maintaining accurate and consistent data is still complex, especially when dealing with multiple data modali…
Label Error Detection