  Developing a robust generalization measure for the performance of machine
learning models is an important and challenging task. A lot of recent research
in the area focuses on the model decision boundary when predicting
generalization. In this paper, as part of the "Predicting Generalization in
Deep Learning" competition, we analyse the complexity of decision boundaries
using the concept of fractal dimension and develop a generalization measure
based on that technique.
