Deep Learning Generalization and the Convex Hull of Training Sets

Understand

We study the generalization of deep learning models in relation to the convex hull of their training sets.

• A trained image classifier basically partitions its domain via decision boundaries and assigns a class to each of those partitions.
• The location of decision boundaries inside the convex hull of training set can be investigated in relation to the training samples.
• However, our analysis shows that in standard image classification datasets, all testing images are considerably outside that convex hull, in the pixel space, in the wavelet space, and in the internal representations learned by deep networks.