A dataset for evaluating one-shot categorization of novel object classes

From just a single example, we can derive quite precise intuitions about what other class members look like. This stands in stark contrast to machine learning algorithms, which typically require tens or even hundreds of thousands of examples to learn a new category. One of the most important open questions in our field is: How do humans achieve this? The stimuli and data provided here (in MATLAB format) are from thousands of crowd-sourced human responses to novel objects. The data can be used to test machine learning generalization as compared to human and also can be used as a test bed for various kinds of category learning models.仅需单个示例，我们便可对同类其他样本的外观形成相当精准的直觉认知。这与机器学习算法形成鲜明对比——后者通常需要数万乃至数十万条示例才能学会一个全新的类别。本领域最重要的开放性问题之一便是：人类是如何实现这一能力的？本次提供的刺激素材与数据（MATLAB格式）源自数千份众包的人类被试对新奇物体的应答结果。该数据集可用于对比测试机器学习与人类的泛化能力，同时也可作为各类类别学习模型的测试平台。