Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features

Understand

Intensive research has been conducted on the verification and validation of deep neural networks (DNNs), aiming to understand if, and how, DNNs can be applied to safety critical applications.

• However, existing verification and validation techniques are limited by their scalability, over both the size of the DNN and the size of the dataset.
• In this paper, we propose a novel abstraction method which abstracts a DNN and a dataset into a Bayesian network (BN).
• We make use of dimensionality reduction techniques to identify hidden features that have been learned by hidden layers of the DNN, and associate each hidden feature with a node of the BN.