论文标题
通过相似性网络进行的度量学习，用于深度半监督学习
Metric learning by Similarity Network for Deep Semi-Supervised Learning
论文作者
论文摘要
由于深度学习的快速发展，深度半监督的学习已在现实世界中广泛实施。最近，注意力已转移到诸如卑鄙的老师之类的方法上，以惩罚两个扰动输入集之间的不一致。尽管这些方法可能会取得积极的结果，但它们忽略了数据实例之间的关系信息。为了解决这个问题，我们提出了一种通过相似性网络（MLSN）称为公制学习的新颖方法，该方法旨在在不同领域适应距离度量。通过与分类网络共同培训，相似性网络可以学习有关成对关系的更多信息，并且在某些经验任务上的表现要比最先进的方法更好。
Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed input sets. Although these methods may achieve positive results, they ignore the relationship information between data instances. To solve this problem, we propose a novel method named Metric Learning by Similarity Network (MLSN), which aims to learn a distance metric adaptively on different domains. By co-training with the classification network, similarity network can learn more information about pairwise relationships and performs better on some empirical tasks than state-of-art methods.
