One-Shot Identification with Different Neural Network Approaches
This work addresses the problem of one-shot learning for computer vision applications, such as industrial tasks and face recognition, but it appears incremental as it builds on existing capsule network methods.
The paper tackled one-shot identification tasks, where predictions are made with limited data, by exploring different neural network approaches, and found that using siamese capsule networks with stacked images achieved strong results, exceeding other techniques on various datasets including industrial applications and face recognition benchmarks.
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.