A Comprehensive Survey of Image Augmentation Techniques for Deep Learning
Deep learning has been achieving decent performance in computer vision
requiring a large volume of images, however, collecting images is expensive and
difficult in many scenarios. To alleviate this issue, many image augmentation
algorithms have been proposed as effective and efficient strategies.
Understanding current algorithms is essential to find suitable methods or
develop novel techniques for given tasks. In this paper, we perform a
comprehensive survey on image augmentation for deep learning with a novel
informative taxonomy. To get the basic idea why we need image augmentation, we
introduce the challenges in computer vision tasks and vicinity distribution.
Then, the algorithms are split into three categories; model-free, model-based,
and optimizing policy-based. The model-free category employs image processing
methods while the model-based method leverages trainable image generation
models. In contrast, the optimizing policy-based approach aims to find the
optimal operations or their combinations. Furthermore, we discuss the current
trend of common applications with two more active topics, leveraging different
ways to understand image augmentation, such as group and kernel theory, and
deploying image augmentation for unsupervised learning. Based on the analysis,
we believe that our survey gives a better understanding helpful to choose
suitable methods or design novel algorithms for practical applications.