Label Quality

Label quality, the accuracy and consistency of annotations in datasets used to train machine learning models, is a critical concern impacting model performance and reliability. Current research focuses on developing automated methods to detect and quantify label errors, employing techniques ranging from regression models to probabilistic predictions from various segmentation and classification models. These efforts aim to improve dataset quality, leading to more robust and accurate machine learning models across diverse applications, including medical imaging and autonomous driving, by enabling efficient error correction and improved model training strategies. The development of standardized benchmarking tools is also a key area of focus to facilitate objective comparison and selection of label quality assessment methods.