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).
@article{arxiv.2010.13233,
title = {Now You See Me (CME): Concept-based Model Extraction},
author = {Dmitry Kazhdan and Botty Dimanov and Mateja Jamnik and Pietro Liò and Adrian Weller},
journal= {arXiv preprint arXiv:2010.13233},
year = {2020}
}
Comments
Presented at the AIMLAI workshop at the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020)