  We address the problem of distribution shifts in test-time data with a
principled data augmentation scheme for the task of content-level
classification. In such a task, properties such as shape or transparency of
test-time containers (cup or drinking glass) may differ from those represented
in the training data. Dealing with such distribution shifts using standard
augmentation schemes is challenging and transforming the training images to
cover the properties of the test-time instances requires sophisticated image
manipulations. We therefore generate diverse augmentations using a family of
max-entropy transformations that create samples with new shapes, colors and
spectral characteristics. We show that such a principled augmentation scheme,
alone, can replace current approaches that use transfer learning or can be used
in combination with transfer learning to improve its performance.
