  Employing machine learning models in the real world requires collecting large
amounts of data, which is both time consuming and costly to collect. A common
approach to circumvent this is to leverage existing, similar data-sets with
large amounts of labelled data. However, models trained on these canonical
distributions do not readily transfer to real-world ones. Domain adaptation and
transfer learning are often used to breach this "reality gap", though both
require a substantial amount of real-world data. In this paper we discuss a
more general approach: we propose learning a general transformation to bring
arbitrary images towards a canonical distribution where we can naively apply
the trained machine learning models. This transformation is trained in an
unsupervised regime, leveraging data augmentation to generate off-canonical
examples of images and training a Deep Learning model to recover their original
counterpart. We quantify the performance of this transformation using
pre-trained ImageNet classifiers, demonstrating that this procedure can recover
half of the loss in performance on the distorted data-set. We then validate the
effectiveness of this approach on a series of pre-trained ImageNet models on a
real world data set collected by printing and photographing images in different
lighting conditions.
