A survey of synthetic data augmentation methods in computer vision

Understand

The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task.

• However, in many scenarios, it is often challenging to obtain sufficient image data for the target task.
• Data augmentation is a way to mitigate this challenge.
• A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance.