← 研踪 PaperTrailarXiv:2504.09341 · v2
Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation
首次提交 2025-04-13 05:04
版本更新 2026-11-11 02:13
雷达观测 2026-11-11 13:07
摘要
Data annotation is an essential but costly aspect of developing machine learning-based technology, requiring multiple human workers to repeatedly label each data point in a data set. We explore the tradeoff between annotation accuracy and cost by identifying minority reports---worker responses disagreeing with the reference majority-vote label---that may indicate unnecessary redundancy, and can therefore be proactively pruned before they are executed. We develop a theoretical framework for choosing the number of repeated annotations required for a task and pruning policies at a target label quality. We estimate minority-report risk on two computer vision datasets re-annotated through a professional platform, then evaluate a rolling policy on the assignment stream to prune over 35% of repeats while preserving more than 99.9% of reference majority-vote labels.
作者备注：43 pages
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-11 02:13 · 最新观测版本
Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation
Data annotation is an essential but costly aspect of developing machine learning-based technology, requiring multiple human workers to repeatedly label each data point in a data set. We explore the tradeoff between annotation accuracy and cost by identifying minority reports---worker responses disagreeing with the reference majority-vote label---that may indicate unnecessary redundancy, and can therefore be proactively pruned before they are executed. We develop a theoretical framework for choosing the number of repeated annotations required for a task and pruning policies at a target label quality. We estimate minority-report risk on two computer vision datasets re-annotated through a professional platform, then evaluate a rolling policy on the assignment stream to prune over 35% of repeats while preserving more than 99.9% of reference majority-vote labels.
查看此版本解读 →查看此版本原文 ↗