Image classification systems recently made a big leap with the advancement of deep neural networks. However, these systems require excessive amount of labeled data in order to be trained properly. This is not always feasible due to several factors, such as expensiveness of labeling process or difficulty of correctly classifying data even for the experts. Because of these practical challenges, label noise is a common problem in datasets and numerous methods to train deep networks with label noise are proposed in literature. Deep networks are known to be relatively robust to label noise, however their tendency to overfit data makes them vulnerable to memorizing even total random noise. Therefore, it is crucial to consider the existence of label noise and develop counter algorithms to fade away its negative effects for training deep neural networks efficiently. Even though an extensive survey of machine learning techniques under label noise exists, literature lacks a comprehensive survey of methodologies specifically centered around deep learning in the presence of noisy labels. This paper aims to present these algorithms while categorizing them according to their similarity in proposed methodology.
翻译:最近,随着深层神经网络的进步,图像分类系统在近期取得了飞跃。然而,这些系统需要过多的标签数据才能得到适当的培训。由于多种因素,这并不总是可行的,例如标签过程费用昂贵,或者即使专家也很难对数据进行正确分类。由于这些实际挑战,标签噪音是数据集中的一个常见问题,文献中也提出了许多培养带有标签噪音的深层网络的方法。深层网络对标签噪音比较强大,尽管它们倾向于过度使用数据使其容易被误读甚至完全随机噪音。因此,必须考虑标签噪音的存在,并开发反算法,以抵消其对深层神经网络培训的消极影响。即使对标签噪音下的机器学习技术进行了广泛调查,文献也缺乏对在噪音标签出现的情况下专门围绕深层学习的方法的全面调查。本文的目的是介绍这些算法,同时根据拟议方法中的相似性对它们进行分类。