  Commonly, machine learning models minimize an empirical expectation. As a
result, the trained models typically perform well for the majority of the data
but the performance may deteriorate in less dense regions of the dataset. This
issue also arises in generative modeling. A generative model may overlook
underrepresented modes that are less frequent in the empirical data
distribution. This problem is known as complete mode coverage. We propose a
sampling procedure based on ridge leverage scores which significantly improves
mode coverage when compared to standard methods and can easily be combined with
any GAN. Ridge leverage scores are computed by using an explicit feature map,
associated with the next-to-last layer of a GAN discriminator or of a
pre-trained network, or by using an implicit feature map corresponding to a
Gaussian kernel. Multiple evaluations against recent approaches of complete
mode coverage show a clear improvement when using the proposed sampling
strategy.
