  Deep Convolutional Neural Networks have made an incredible progress in many
Computer Vision tasks. This progress, however, often relies on the availability
of large amounts of the training data, required to prevent over-fitting, which
in many domains entails significant cost of manual data labeling. An
alternative approach is application of data augmentation (DA) techniques that
aim at model regularization by creating additional observations from the
available ones. This survey focuses on two DA research streams: image mixing
and automated selection of augmentation strategies. First, the presented
methods are briefly described, and then qualitatively compared with respect to
their key characteristics. Various quantitative comparisons are also included
based on the results reported in recent DA literature. This review mainly
covers the methods published in the materials of top-tier conferences and in
leading journals in the years 2017-2021.
