Figure 5.

Illustration of the augmentation pipeline for a generative-model-based data augmentation. The input data, x, are fed into the generative model, g, which synthesizes additional data samples to augment the training set. The downstream architecture, e, which may take the form of a convolutional neural network or U-Net, is then trained on a combination of the synthesized data and real data from the training set. The training set is split into training and validation sets, where the validation set contains only real data for evaluation purposes. After training, the model can be evaluated using various test sets.