  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.
