Identifying Model Weakness with Adversarial Examiner

Understand

Machine learning models are usually evaluated according to the average case performance on the test set.

• However, this is not always ideal, because in some sensitive domains (e.g.
• autonomous driving), it is the worst case performance that matters more.
• In this paper, we are interested in systematic exploration of the input data space to identify the weakness of the model to be evaluated.