Siamese Basis Function Networks for Data-efficient Defect Classification in Technical Domains
This work provides a solution for data-efficient defect classification, particularly beneficial for industries and researchers facing limited data availability in technical domains, offering an incremental improvement over existing methods.
This paper addresses data scarcity in technical domains for defect classification by proposing a novel Siamese Basis Function Network. The method achieves superior performance compared to state-of-the-art models (ResNet50 and ResNet101) in low-data regimes across three technical datasets (NEU, BSD, TEX) and two classical datasets (CIFAR-10, MNIST).
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.