  Deep Neural Networks (DNNs) have achieved remarkable performance on a range
of tasks. A key step to further empowering DNN-based approaches is improving
their explainability. In this work we present CME: a concept-based model
extraction framework, used for analysing DNN models via concept-based extracted
models. Using two case studies (dSprites, and Caltech UCSD Birds), we
demonstrate how CME can be used to (i) analyse the concept information learned
by a DNN model (ii) analyse how a DNN uses this concept information when
predicting output labels (iii) identify key concept information that can
further improve DNN predictive performance (for one of the case studies, we
showed how model accuracy can be improved by over 14%, using only 30% of the
available concepts).
