@InProceedings{10.1007/978-3-031-46221-4_10,
author="Mohr, Janis
and Frochte, J{\"o}rg",
editor="Garibaldi, Jonathan
and Wagner, Christian
and B{\"a}ck, Thomas
and Lam, Hak-Keung
and Cottrell, Marie
and Madani, Kurosh
and Warwick, Kevin",
title="One-Shot Identification with Different Neural Network Approaches",
booktitle="Computational Intelligence",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="205--222",
abstract="Convolutional neural networks (CNNs) have been widely used in the computer vision community, significantly improving the state-of-the-art. But learning good features often is computationally expensive in machine learning settings and is especially difficult when there is a lack of data. One-shot learning is one such area where only limited data is available. In one-shot learning, predictions have to be made after seeing only one example from one class, which requires special techniques. In this paper we explore different approaches to one-shot identification tasks in different domains including an industrial application and face recognition. We use a special technique with stacked images and use siamese capsule networks. It is encouraging to see that the approach using capsule architecture achieves strong results and exceeds other techniques on a wide range of datasets from industrial application to face recognition benchmarks while being easy to use and optimise.",
isbn="978-3-031-46221-4"
}