  Self-explaining deep models are designed to learn the latent concept-based
explanations implicitly during training, which eliminates the requirement of
any post-hoc explanation generation technique. In this work, we propose one
such model that appends an explanation generation module on top of any basic
network and jointly trains the whole module that shows high predictive
performance and generates meaningful explanations in terms of concepts. Our
training strategy is suitable for unsupervised concept learning with much
lesser parameter space requirements compared to baseline methods. Our proposed
model also has provision for leveraging self-supervision on concepts to extract
better explanations. However, with full concept supervision, we achieve the
best predictive performance compared to recently proposed concept-based
explainable models. We report both qualitative and quantitative results with
our method, which shows better performance than recently proposed concept-based
explainability methods. We reported exhaustive results with two datasets
without ground truth concepts, i.e., CIFAR10, ImageNet, and two datasets with
ground truth concepts, i.e., AwA2, CUB-200, to show the effectiveness of our
method for both cases. To the best of our knowledge, we are the first ante-hoc
explanation generation method to show results with a large-scale dataset such
as ImageNet.
