The increasing impact of black box models, and particularly of unsupervised
ones, comes with an increasing interest in tools to understand and interpret
them. In this paper, we consider in particular how to characterise visual
groupings discovered automatically by deep neural networks, starting with
state-of-the-art clustering methods. In some cases, clusters readily correspond
to an existing labelled dataset. However, often they do not, yet they still
maintain an "intuitive interpretability". We introduce two concepts, visual
learnability and describability, that can be used to quantify the
interpretability of arbitrary image groupings, including unsupervised ones. The
idea is to measure (1) how well humans can learn to reproduce a grouping by
measuring their ability to generalise from a small set of visual examples
(learnability) and (2) whether the set of visual examples can be replaced by a
succinct, textual description (describability). By assessing human annotators
as classifiers, we remove the subjective quality of existing evaluation
metrics. For better scalability, we finally propose a class-level captioning
system to generate descriptions for visual groupings automatically and compare
it to human annotators using the describability metric.

通过评估人类注释者作为分类器，本研究提出了视觉可学性和可描述性两个概念，用于量化任意图像分组（包括无监督分组）的可解释性，并提出了一种类别级别的字幕生成系统来自动生成对视觉分组的描述，并使用可描述性度量将其与人类注释者进行比较。