CycleCluster：深度半监督分类的现代化聚类正则化
CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification
摘要：
鉴于获得大量标记数据的潜在困难，许多工作已经探索了深度半监督学习的使用，其使用标记和未标记的数据来训练神经网络架构。绝大多数SSL方法集中于实现低密度分离假设或一致性假设，即决策边界应该位于低密度区域。然而，他们通过对每个数据点的决策边界进行局部更改来实现这一假设，而忽略了数据的全局结构。在这项工作中，我们探索了一种替代方法，使用集群数据中的全局信息来更新我们的决策边界。我们提出了一个新的框架，CycleCluster，用于深度半监督分类。我们的核心优化是由一个新的基于聚类的正则化以及一个基于图的伪标签和一个共享的深度网络驱动的。证明直接实现集群假设是一个可行的替代流行的一致性为基础的正规化。我们证明了我们的技术的预测能力，通过一组仔细的数值结果。
英文摘要：
Given the potential difficulties in obtaining large quantities of labelled data, many works have explored the use of deep semi-supervised learning, which uses both labelled and unlabelled data to train a neural network architecture. The vast majority of SSL approaches focus on implementing the low-density separation assumption or consistency assumption, the idea that decision boundaries should lie in low density regions. However, they have implemented this assumption by making local changes to the decision boundary at each data point, ignoring the global structure of the data. In this work, we explore an alternative approach using the global information present in the clustered data to update our decision boundaries. We propose a novel framework, CycleCluster, for deep semi-supervised classification. Our core optimisation is driven by a new clustering based regularisation along with a graph based pseudo-labels and a shared deep network. Demonstrating that direct implementation of the cluster assumption is a viable alternative to the popular consistency based regularisation. We demonstrate the predictive capability of our technique through a careful set of numerical results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
