论文标题
多级零射门学习，用于艺术材料识别
Multi-Class Zero-Shot Learning for Artistic Material Recognition
论文作者
论文摘要
零射击学习（ZSL）是转移学习的一种极端形式，在培训阶段，没有提供要分类的数据的标签示例。取而代之的是，ZSL使用有关该领域的其他信息，并依靠转移学习算法来推断有关缺失实例的知识。 ZSL方法是稀疏数据集的有吸引力的解决方案。在这里，我们概述了一个模型，以通过了解对作品主题的英语描述及其复合材料之间的关系来确定创建艺术品的材料。在尝试了一系列超参数之后，我们产生了一个模型，该模型能够正确识别完全不同博物馆数据集中使用的材料。该模型返回了从TATE Collection获取的5,000件艺术品的分类精度为48.42％，这与用于创建和训练我们的模型的Rijksmuseum网络不同。
Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no labelled examples of the data to be classified are provided during the training stage. Instead, ZSL uses additional information learned about the domain, and relies upon transfer learning algorithms to infer knowledge about the missing instances. ZSL approaches are an attractive solution for sparse datasets. Here we outline a model to identify the materials with which a work of art was created, by learning the relationship between English descriptions of the subject of a piece and its composite materials. After experimenting with a range of hyper-parameters, we produce a model which is capable of correctly identifying the materials used on pieces from an entirely distinct museum dataset. This model returned a classification accuracy of 48.42% on 5,000 artworks taken from the Tate collection, which is distinct from the Rijksmuseum network used to create and train our model.
