论文标题
从弱监督的学习到杂质学习：简介
From Weakly Supervised Learning to Biquality Learning: an Introduction
论文作者
论文摘要
弱监督学习（WSL）的领域最近看到了流行的激增，许多论文涉及不同类型的“监督缺陷”。在WSL用例中，存在各种情况，其中收集的“信息”不完美。 WSL的范式试图通过相关解决方案列出和解决这些问题。在本文中，我们回顾了WSL的研究进度，目的是将其作为对该领域的简要介绍。我们介绍了WSL立方体的三个轴以及其各个方面的大多数元素的概述。我们提出了三个可测量的数量，这些数量是先前定义的立方体中的坐标：质量，适应性和信息数量。因此，我们建议可以将双质量学习框架定义为WSL立方体的计划，并建议将以前在WSL文献中以统一的生物学习文献为单位重新发现以前无关的补丁。
The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies". In WSL use cases, a variety of situations exists where the collected "information" is imperfect. The paradigm of WSL attempts to list and cover these problems with associated solutions. In this paper, we review the research progress on WSL with the aim to make it as a brief introduction to this field. We present the three axis of WSL cube and an overview of most of all the elements of their facets. We propose three measurable quantities that acts as coordinates in the previously defined cube namely: Quality, Adaptability and Quantity of information. Thus we suggest that Biquality Learning framework can be defined as a plan of the WSL cube and propose to re-discover previously unrelated patches in WSL literature as a unified Biquality Learning literature.
