Adventures In Deep Learning Geometry

Deep learning models are pervasive for a multitude of tasks, but the complexity of these models can limit interpretation and inhibit trust. For a classification task, we investigate the induced relationships between the class conditioned data distributions, and geometrically compare/contrast the data with the deep learning models' output weight vectors. These geometric relationships are examined across models as a function of dense hidden layer width. Additionally, we geometrically characterize perturbation-based adversarial examples with respect to the deep learning model.