论文标题
暹罗基函数网络，用于技术域中的数据有效缺陷分类
Siamese Basis Function Networks for Data-efficient Defect Classification in Technical Domains
论文作者
论文摘要
培训技术领域中的深度学习模型通常伴随着挑战，即尽管任务清楚，但可用于培训数据不足。在这项工作中，我们提出了一种基于暹罗网络和径向基础函数网络组合的新方法，以执行数据有效的分类，而不会通过以数据有效的方式测量语义空间中图像之间的距离。我们使用三个技术数据集（NEU数据集，BSD数据集和TEX数据集）开发模型。除技术领域外，我们还显示了对经典数据集（CIFAR10和MNIST）的一般适用性。通过逐步减少可用于培训的样品数量，对该方法进行了针对最新模型（RESNET50和RESNET101）的测试。作者表明，所提出的方法的表现优于低数据制度中最先进的模型。
Training deep learning models in technical domains is often accompanied by the challenge that although the task is clear, insufficient data for training is available. In this work, we propose a novel approach based on the combination of Siamese networks and radial basis function networks to perform data-efficient classification without pretraining by measuring the distance between images in semantic space in a data-efficient manner. We develop the models using three technical datasets, the NEU dataset, the BSD dataset, and the TEX dataset. In addition to the technical domain, we show the general applicability to classical datasets (cifar10 and MNIST) as well. The approach is tested against state-of-the-art models (Resnet50 and Resnet101) by stepwise reduction of the number of samples available for training. The authors show that the proposed approach outperforms the state-of-the-art models in the low data regime.
