使用高噪声标签进行深度学习分类（CS LG）---刘持诚

深度学习系统在图像分类方面表现出了超高的准确性，但代价是需要收集大量的图像数据集。收集如此大量的数据会导致训练集中的标签错误。对多媒体内容进行检索、分类或推荐的索引可能涉及到基于多个标准的标签化或分类。在我们的案例中，我们用一个封闭的角色身份数据集来训练人脸识别系统，用于演员识别，同时暴露在大量的扰动器（我们的数据库中未知的演员）。众所周知，人脸分类器对标签噪声很敏感。同时，我们回顾了最近关于如何在训练深度学习分类器时管理噪声注释的工作，不过这与我们对人脸识别的研究无关。

原文题目：Deep Learning Classification With Noisy Labels

原文：Deep Learning systems have shown tremendous accuracy in image classification, at the cost of big image datasets. Collecting such amounts of data can lead to labelling errors in the training set. Indexing multimedia content for retrieval, classification or recommendation can involve tagging or classification based on multiple criteria. In our case, we train face recognition systems for actors identification with a closed set of identities while being exposed to a significant number of perturbators (actors unknown to our database). Face classifiers are known to be sensitive to label noise. We review recent works on how to manage noisy annotations when training deep learning classifiers, independently from our interest in face recognition.

原文作者：Guillaume Sanchez, Vincente Guis, Ricard Marxer, Frédéric Bouchara

原文地址：https://arxiv.org/abs/2004.11116