  Supervised deep learning relies on the assumption that enough training data
is available, which presents a problem for its application to several fields,
like medical imaging. On the example of a binary image classification task
(breast cancer recognition), we show that pretraining a generative model for
meaningful image augmentation helps enhance the performance of the resulting
classifier. By augmenting the data, performance on downstream classification
tasks could be improved even with a relatively small training set. We show that
this "adversarial augmentation" yields promising results compared to classical
image augmentation on the example of breast cancer classification.
