  Data augmentation is one of the most important tools in training modern deep
neural networks. Recently, great advances have been made in searching for
optimal augmentation policies in the image classification domain. However, two
key points related to data augmentation remain uncovered by the current
methods. First is that most if not all modern augmentation search methods are
offline and learning policies are isolated from their usage. The learned
policies are mostly constant throughout the training process and are not
adapted to the current training model state. Second, the policies rely on
class-preserving image processing functions. Hence applying current offline
methods to new tasks may require domain knowledge to specify such kind of
operations. In this work, we offer an orthogonal online data augmentation
scheme together with three new augmentation networks, co-trained with the
target learning task. It is both more efficient, in the sense that it does not
require expensive offline training when entering a new domain, and more
adaptive as it adapts to the learner state. Our augmentation networks require
less domain knowledge and are easily applicable to new tasks. Extensive
experiments demonstrate that the proposed scheme alone performs on par with the
state-of-the-art offline data augmentation methods, as well as improving upon
the state-of-the-art in combination with those methods. Code is available at
https://github.com/zhiqiangdon/online-augment .
