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).

本论文提出 CME 框架，用于通过基于概念的提取模型分析 DNN 模型，通过 dSprites 和 Caltech UCSD Birds 的两个案例研究表明，CME 可用于分析 DNN 模型学习的概念信息，如何使用这些概念信息来预测输出标签，识别关键的概念信息，进一步提高 DNN 的预测性能，准确率可提高 14％以上，仅使用可用概念的 30％。