  Interpretability of Deep Neural Networks using concept-based models offers a
promising way to explain model behavior through human-understandable concepts.
A parallel line of research focuses on disentangling the data distribution into
its underlying generative factors, in turn explaining the data generation
process. While both directions have received extensive attention, little work
has been done on explaining concepts in terms of generative factors to unify
mathematically disentangled representations and human-understandable concepts
as an explanation for downstream tasks. In this paper, we propose a novel
method CoLiDR - which utilizes a disentangled representation learning setup for
learning mutually independent generative factors and subsequently learns to
aggregate the said representations into human-understandable concepts using a
novel aggregation/decomposition module. Experiments are conducted on datasets
with both known and unknown latent generative factors. Our method successfully
aggregates disentangled generative factors into concepts while maintaining
parity with state-of-the-art concept-based approaches. Quantitative and visual
analysis of the learned aggregation procedure demonstrates the advantages of
our work compared to commonly used concept-based models over four challenging
datasets. Lastly, our work is generalizable to an arbitrary number of concepts
and generative factors - making it flexible enough to be suitable for various
types of data.
