Unsupervised black-box models are challenging to interpret. Indeed, most
existing explainability methods require labels to select which component(s) of
the black-box's output to interpret. In the absence of labels, black-box
outputs often are representation vectors whose components do not correspond to
any meaningful quantity. Hence, choosing which component(s) to interpret in a
label-free unsupervised/self-supervised setting is an important, yet unsolved
problem. To bridge this gap in the literature, we introduce two crucial
extensions of post-hoc explanation techniques: (1) label-free feature
importance and (2) label-free example importance that respectively highlight
influential features and training examples for a black-box to construct
representations at inference time. We demonstrate that our extensions can be
successfully implemented as simple wrappers around many existing feature and
example importance methods. We illustrate the utility of our label-free
explainability paradigm through a qualitative and quantitative comparison of
representation spaces learned by various autoencoders trained on distinct
unsupervised tasks.

该文介绍了两种关键的后处理解释技术扩展：无标签特征重要性和无标签示例重要性，用于黑盒模型在推断时构造表示，并通过定性和定量比较展示了这种无标签可解释性模式的效用。