用胶囊网络实现仅看一张图就识别，效果优于传统方法。
One-Shot Identification with Different Neural Network Approaches
- 采用堆叠图像与孪生胶囊网络结构
- 在工业与人脸数据集上均超越现有技术
- 方法易用且优化简单，适合数据稀缺场景
卷积神经网络（CNN）在计算机视觉领域广泛应用，显著提升了性能。但在数据有限的场景下，学习有效特征往往计算成本高。一类典型挑战是一次性学习（one-shot learning），即仅见一个样本即需完成分类任务，需特殊处理。本文探索了多种一次性识别方法，涵盖工业应用和人脸识别场景。通过使用堆叠图像与孪生胶囊网络，结果表明：基于胶囊架构的方法在多个数据集（从工业应用到人脸识别基准）上表现优异，不仅性能领先，且易于使用和优化。
原文摘要 · Abstract (English)
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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