  Neural networks are prone to overfitting and memorizing data patterns. To
avoid over-fitting and enhance their generalization and performance, various
methods have been suggested in the literature, including dropout,
regularization, label smoothing, etc. One such method is augmentation which
introduces different types of corruption in the data to prevent the model from
overfitting and to memorize patterns present in the data. A sub-area of data
augmentation is image mixing and deleting. This specific type of augmentation
either deletes image regions or mixes two images to hide or make particular
characteristics of images confusing for the network, forcing it to emphasize
the overall structure of the object in an image. Models trained with this
approach have proven to perform and generalize well compared to those trained
without image mixing or deleting. An added benefit that comes with this method
of training is robustness against image corruption. Due to its low
computational cost and recent success, researchers have proposed many image
mixing and deleting techniques. We furnish an in-depth survey of image mixing
and deleting techniques and provide categorization via their most
distinguishing features. We initiate our discussion with some fundamental
relevant concepts. Next, we present essentials, such as each category's
strengths and limitations, describing their working mechanism, basic
formulations, and applications. We also discuss the general challenges and
recommend possible future research directions for image mixing and deleting
data augmentation techniques. Datasets and codes for evaluation are publicly
available here.
