论文标题
深度半监督学习的概述
An Overview of Deep Semi-Supervised Learning
论文作者
论文摘要
深度神经网络表明，当经过广泛的标记数据集（例如Imagenet）培训时，他们有能力在广泛的监督学习任务（例如，图像分类）上提供出色的性能（例如，图像分类）。但是，创建如此大的数据集需要大量资源，时间和精力。在许多实际情况下，可能无法提供此类资源，从而限制了许多深度学习方法的采用和应用。在寻找更有效的深度学习方法以克服对大型注释数据集的需求时，对半监督学习的研究兴趣越来越不断增加，及其在深层神经网络上的应用，以减少所需的标记数据量，或者通过开发新型方法或采用现有的半熟悉学习框架来减少对深度学习设置的现有学习框架。在本文中，我们提供了深度半监督学习的全面概述，首先是对该领域的介绍，然后摘要对深度学习中主要的半监督方法进行了摘要。
Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and the application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome the need for large annotated datasets, there is a rising research interest in semi-supervised learning and its applications to deep neural networks to reduce the amount of labeled data required, by either developing novel methods or adopting existing semi-supervised learning frameworks for a deep learning setting. In this paper, we provide a comprehensive overview of deep semi-supervised learning, starting with an introduction to the field, followed by a summarization of the dominant semi-supervised approaches in deep learning.
