Decision Boundary of Deep Neural Networks: Challenges and Opportunities

Abstract

One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Trust can be improved once we understand how and why deep models carve out a particular form of decision boundary and thus make particular decisions. Robustness against adversarial examples is directly related to the decision boundary as adversarial examples are basically 'missed out' by the decision boundary between two classes. Investigating the decision boundary of deep neural networks, nevertheless, faces tremendous challenges. First, how we can generate instances near the decision boundary that are similar to real samples? Second, how we can leverage near decision boundary instances to characterize the behaviour of deep neural networks? Motivated to solve these challenges, we focus on investigating the decision boundary of deep neural network classifiers. In particular, we propose a novel approach to generate instances near decision boundary of pre-trained DNNs and then leverage these instances to characterize the behaviour of deep models.