Figure 2.

Flowchart presenting training- and test-time data augmentation. In the training-time data augmentation approach, we generate synthetic data to increase the representativeness of a training set (and ultimately build better models), whereas in test-time augmentation, we benefit from the ensemble-like technique, in which multiple homogeneous classifiers vote for the final class label for an incoming example by classifying this sample and a number of its augmented versions.