  There is growing interest in concept-based models (CBMs) that combine
high-performance and interpretability by acquiring and reasoning with a
vocabulary of high-level concepts. A key requirement is that the concepts be
interpretable. Existing CBMs tackle this desideratum using a variety of
heuristics based on unclear notions of interpretability, and fail to acquire
concepts with the intended semantics. We address this by providing a clear
definition of interpretability in terms of alignment between the model's
representation and an underlying data generation process, and introduce
GlanceNets, a new CBM that exploits techniques from disentangled representation
learning and open-set recognition to achieve alignment, thus improving the
interpretability of the learned concepts. We show that GlanceNets, paired with
concept-level supervision, achieve better alignment than state-of-the-art
approaches while preventing spurious information from unintendedly leaking into
the learned concepts.
