# One-Shot Identification with Different Neural Network Approaches

arXiv: https://arxiv.org/abs/2601.08278 · HTML: https://mlsanity.com/paper/2601.08278/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Deep Learning Face Representation by Joint Identification-Verification](https://mlsanity.com/paper/1406.4773/) (2014)
- [Unsupervised Learning of Object Structure and Dynamics from Videos](https://mlsanity.com/paper/1906.07889/) (2019)
- [DeepCaps: Going Deeper with Capsule Networks](https://mlsanity.com/paper/1904.09546/) (2019)
- [Capsule Network Performance on Complex Data](https://mlsanity.com/paper/1712.03480/) (2017)
- [Brain Tumor Type Classification via Capsule Networks](https://mlsanity.com/paper/1802.10200/) (2018)
- [A Capsule Network for Recommendation and Explaining What You Like and Dislike](https://mlsanity.com/paper/1907.00687/) (2019)
- [One-Shot Recognition of Manufacturing Defects in Steel Surfaces](https://mlsanity.com/paper/2005.05815/) (2020)
- [Capsule Networks for Low Resource Spoken Language Understanding](https://mlsanity.com/paper/1805.02922/) (2018)
- [Human vs. supervised machine learning: Who learns patterns faster?](https://mlsanity.com/paper/2012.03661/) (2020)
- [Fast CapsNet for Lung Cancer Screening](https://mlsanity.com/paper/1806.07416/) (2018)
- [Improved Explainability of Capsule Networks: Relevance Path by Agreement](https://mlsanity.com/paper/1802.10204/) (2018)
- [Convolutional capsule network for classification of breast cancer histology images](https://mlsanity.com/paper/1804.08376/) (2018)
- [Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks](https://mlsanity.com/paper/1805.04424/) (2018)

## Abstract (arXiv)

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.