  Concept-based explanations have emerged as a popular way of extracting
human-interpretable representations from deep discriminative models. At the
same time, the disentanglement learning literature has focused on extracting
similar representations in an unsupervised or weakly-supervised way, using deep
generative models. Despite the overlapping goals and potential synergies, to
our knowledge, there has not yet been a systematic comparison of the
limitations and trade-offs between concept-based explanations and
disentanglement approaches. In this paper, we give an overview of these fields,
comparing and contrasting their properties and behaviours on a diverse set of
tasks, and highlighting their potential strengths and limitations. In
particular, we demonstrate that state-of-the-art approaches from both classes
can be data inefficient, sensitive to the specific nature of the
classification/regression task, or sensitive to the employed concept
representation.
