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.
Abstract (translated)
卷积神经网络（CNNs）在计算机视觉社区中得到了广泛应用，显著提升了技术前沿。然而，在机器学习环境中学习良好的特征通常需要较高的计算成本，尤其是在数据不足的情况下尤其困难。一次性学习就是这样一个领域，仅提供有限的数据。在一次性学习中，模型必须基于看到的一个类别的单一示例进行预测，这要求使用特殊的技术。 本文探讨了不同的一次性识别任务方法，涵盖工业应用和人脸识别等不同领域。我们采用了一种特殊的叠图技术，并利用孪生胶囊网络（Siamese Capsule Networks）来解决这些问题。令人鼓舞的是，基于胶囊架构的方法在从工业应用到人脸识别基准测试的广泛数据集上取得了优异的结果，并且易于使用和优化，超过了其他方法的表现。
URL
https://arxiv.org/abs/2601.08278
