论文标题
人类可以进行比一对一的学习吗？
Can Humans Do Less-Than-One-Shot Learning?
论文作者
论文摘要
能够从少量数据中学习是人类智能的关键特征，但是{\ em} small agg}在本文中，我们介绍了一种新型的实验范式，使我们能够在非常数据筛选的设置中检查分类，询问人类是否可以学习比拥有典范更多的类别（即，人类可以做“比一体的镜头”，“学习”吗？）。使用这种范式进行的实验表明，人们能够在这种情况下学习，并为基本机制提供了一些见解。首先，人们可以准确地推断并表示很少的数据中的高维特征空间。其次，在推断相关的空间后，人们使用一种基于原型的分类形式（与基于示例的分类）来进行分类推断。最后，响应中的系统，机器可爱的模式表明，人们可能具有有效的诱导偏见来处理这类数据筛选问题。
Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they have exemplars (i.e., can humans do "less-than-one shot" learning?). An experiment conducted using this paradigm reveals that people are capable of learning in such settings, and provides several insights into underlying mechanisms. First, people can accurately infer and represent high-dimensional feature spaces from very little data. Second, having inferred the relevant spaces, people use a form of prototype-based categorization (as opposed to exemplar-based) to make categorical inferences. Finally, systematic, machine-learnable patterns in responses indicate that people may have efficient inductive biases for dealing with this class of data-scarce problems.
