论文标题
通过最佳运输进行半监督学习的最佳传输匹配分布
Matching Distributions via Optimal Transport for Semi-Supervised Learning
论文作者
论文摘要
半监督学习（SSL）方法是在培训过程中没有足够数量的标记数据时，是使用未标记数据的有影响力的框架。基于卷积神经网络（CNN）的SSL方法最近为标准基准任务（例如图像分类）提供了成功的结果。在这项工作中，我们考虑了SSL问题的一般设置，其中标记和未标记的数据来自相同的潜在概率分布。我们提出了一种新方法，该方法采用了最佳运输（OT）技术，作为离散经验概率指标之间相似性的指标，以为未标记的数据提供伪标签，然后可以将其与初始标记的数据结合使用，以SSL模型以SSL模型训练CNN。我们已经评估并将我们提出的方法与标准数据集上的最先进的SSL算法进行了比较，以证明我们的SSL算法的优势和有效性。
Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training. SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful results on standard benchmark tasks such as image classification. In this work, we consider the general setting of SSL problem where the labeled and unlabeled data come from the same underlying probability distribution. We propose a new approach that adopts an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures to provide pseudo-labels for the unlabeled data, which can then be used in conjunction with the initial labeled data to train the CNN model in an SSL manner. We have evaluated and compared our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our SSL algorithm.
