零次学习是AI识别方法之一。简单来说就是识别从未见过的数据类别,即训练的分类器不仅仅能够识别出训练集中已有的数据类别,还可以对于来自未见过的类别的数据进行区分。这是一个很有用的功能,使得计算机能够具有知识迁移的能力,并无需任何训练数据,很符合现实生活中海量类别的存在形式。

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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.

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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.

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