Feb. 12, 2024, 5:43 a.m. | Amir Ziai Aneesh Vartakavi

cs.LG updates on arXiv.org arxiv.org

High-quality and consistent annotations are fundamental to the successful development of robust machine learning models. Traditional data annotation methods are resource-intensive and inefficient, often leading to a reliance on third-party annotators who are not the domain experts. Hard samples, which are usually the most informative for model training, tend to be difficult to label accurately and consistently without business context. These can arise unpredictably during the annotation process, requiring a variable number of iterations and rounds of feedback, leading to …

active learning annotation annotations building classifiers consistent cs.cv cs.lg data data annotation development domain domain experts experts framework language language models machine machine learning machine learning models quality reliance robust samples video vision vision-language models

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