  Developing inherently interpretable models for prediction has gained
prominence in recent years. A subclass of these models, wherein the
interpretable network relies on learning high-level concepts, are valued
because of closeness of concept representations to human communication.
However, the visualization and understanding of the learnt unsupervised
dictionary of concepts encounters major limitations, specially for large-scale
images. We propose here a novel method that relies on mapping the concept
features to the latent space of a pretrained generative model. The use of a
generative model enables high quality visualization, and naturally lays out an
intuitive and interactive procedure for better interpretation of the learnt
concepts. Furthermore, leveraging pretrained generative models has the
additional advantage of making the training of the system more efficient. We
quantitatively ascertain the efficacy of our method in terms of accuracy of the
interpretable prediction network, fidelity of reconstruction, as well as
faithfulness and consistency of learnt concepts. The experiments are conducted
on multiple image recognition benchmarks for large-scale images. Project page
available at https://jayneelparekh.github.io/VisCoIN_project_page/
