Among the three main components (data, labels, and models) of any supervised
learning system, data and models have been the main subjects of active
research. However, studying labels and their properties has received very
little attention. Current principles and paradigms of labeling impose several
challenges to machine learning algorithms. Labels are often incomplete,
ambiguous, and redundant. In this paper we study the effects of various
properties of labels and introduce the Label Refinery: an iterative procedure
that updates the ground truth labels after examining the entire dataset. We
show significant gain using refined labels across a wide range of models. Using
a Label Refinery improves the state-of-the-art top-1 accuracy of (1) AlexNet
from 59.3 to 67.2, (2) MobileNet from 70.6 to 73.39, (3) MobileNet-0.25 from
50.6 to 55.59, (4) VGG19 from 72.7 to 75.46, and (5) Darknet19 from 72.9 to
74.47.

本文研究标签的一些属性对于监督学习算法的挑战，介绍了 Label Refinery，这是一个迭代的过程，可以在检查整个数据集后更新基本事实标签。作者发现，使用精炼标签可以提高各种模型的准确性，如 AlexNet、MobileNet 和 VGG19 等。