One-Shot Identification with Different Neural Network Approaches

Janis Mohr; Jörg Frochte

doi:10.1007/978-3-031-46221-4_10

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arXiv · 2601.08278

One-Shot Identification with Different Neural Network Approaches

Janis Mohr·Jörg Frochte

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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.

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BibTeX RIS

Janis Mohr, Jörg Frochte. 2026-01-13. One-Shot Identification with Different Neural Network Approaches. https://doi.org/10.1007/978-3-031-46221-4_10

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