  In many classification problems, we want a classifier that is robust to a
range of non-semantic transformations. For example, a human can identify a dog
in a picture regardless of the orientation and pose in which it appears. There
is substantial evidence that this kind of invariance can significantly improve
the accuracy and generalization of machine learning models. A common technique
to teach a model geometric invariances is to augment training data with
transformed inputs. However, which invariances are desired for a given
classification task is not always known. Determining an effective data
augmentation policy can require domain expertise or extensive data
pre-processing. Recent efforts like AutoAugment optimize over a parameterized
search space of data augmentation policies to automate the augmentation
process. While AutoAugment and similar methods achieve state-of-the-art
classification accuracy on several common datasets, they are limited to
learning one data augmentation policy. Often times different classes or
features call for different geometric invariances. We introduce Dynamic Network
Augmentation (DNA), which learns input-conditional augmentation policies.
Augmentation parameters in our model are outputs of a neural network and are
implicitly learned as the network weights are updated. Our model allows for
dynamic augmentation policies and performs well on data with geometric
transformations conditional on input features.
