One-Shot Identification with Different Neural Network Approaches

TL;DR

This paper explores various neural network approaches for one-shot identification, demonstrating that capsule networks with stacked images outperform other methods across multiple domains including industrial and face recognition tasks.

Contribution

The paper introduces the use of siamese capsule networks with stacked images for one-shot learning, showing superior performance over other techniques.

Findings

Capsule networks achieve strong results in one-shot identification.

The approach outperforms other methods on diverse datasets.

The method is easy to use and optimize.

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.