  The usage of medical image data for the training of large-scale machine
learning approaches is particularly challenging due to its scarce availability
and the costly generation of data annotations, typically requiring the
engagement of medical professionals. The rapid development of generative models
allows towards tackling this problem by leveraging large amounts of realistic
synthetically generated data for the training process. However, randomly
choosing synthetic samples, might not be an optimal strategy.
  In this work, we investigate the targeted generation of synthetic training
data, in order to improve the accuracy and robustness of image classification.
Therefore, our approach aims to guide the generative model to synthesize data
with high epistemic uncertainty, since large measures of epistemic uncertainty
indicate underrepresented data points in the training set. During the image
generation we feed images reconstructed by an auto encoder into the classifier
and compute the mutual information over the class-probability distribution as a
measure for uncertainty.We alter the feature space of the autoencoder through
an optimization process with the objective of maximizing the classifier
uncertainty on the decoded image. By training on such data we improve the
performance and robustness against test time data augmentations and adversarial
attacks on several classifications tasks.
