U-Deepdig: Scalable Deep Decision Boundary Instance Generation

For more than a decade, deep learning algorithms have consistently achieved and improved upon the state-of-the-art performance on image classification tasks. However, there is a general lack of understanding and knowledge about the decision boundaries carved by these modern deep neural network architectures. Recently, an algorithm called DeepDIG was introduced to generate boundary instances between two classes based on the decision regions defined by any deep neural network classifier. Although it is very effective in generating boundary instances, the underlying algorithm was designed to work with two classes at a time in a non-commutative fashion which makes it ill-suited for multi-class problems with hundreds of classes. In this work, we extend the DeepDIG algorithm so that it scales linearly with the number of classes. We show that the proposed U-DeepDIG algorithm maintains the efficacy of the original DeepDIG algorithm while being scalable and more efficient when applied to larger classification problems. We demonstrate this by applying our algorithm on MNIST, Fashion-MNIST and CIFAR10 datasets. In addition to qualitative comparisons, we also perform extensive quantitative comparison by analyzing the margin between the class boundaries and the instances generated.