Output Range Analysis for Deep Neural Networks

Understand

Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems.

• Increasingly, these deep NNs are also been deployed in high-assurance applications.
• Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied.
• In this paper, we study a specific verification problem of computing a guaranteed range for the output of a deep neural network given a set of inputs represented as a convex polyhedron.