Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues.
翻译:零光学习(ZSL)旨在预测在培训期间从未使用外部知识(a.k.a.侧面信息)的班级,已经进行了广泛的调查。在本文中,我们从外部知识的角度介绍了对ZSL的文献审查,我们从外部知识的角度对外部知识进行分类,审查它们的方法,比较不同的外部知识。在文献审查中,我们进一步讨论和展望象征性知识在解决ZSL和其他机器学习短缺抽样问题中的作用。