Many tasks such as retrieval and recommendations can significantly benefit
from structuring the data, commonly in a hierarchical way. To achieve this
through annotations of high dimensional data such as images or natural text can
be significantly labor intensive. We propose an approach for uncovering the
hierarchical structure of data based on efficient discriminative testing rather
than annotations of individual datapoints. Using two-alternative-forced-choice
(2AFC) testing and deep metric learning we achieve embedding of the data in
semantic space where we are able to successfully hierarchically cluster. We
actively select triplets for the 2AFC test such that the modeling process is
highly efficient with respect to the number of tests presented to the
annotator. We empirically demonstrate the feasibility of the method by
confirming the shape bias on synthetic data and extract hierarchical structure
on the Fashion-MNIST dataset to a finer granularity than the original labels.

提出一种通过高效的区分测试而非逐个数据点的注释来揭示数据的层次结构的方法，利用二选一强制选择测试和深度度量学习将数据嵌入语义空间，并通过实验证明了该方法的可行性。