GANs can be trained on existing datasets and used to generate synthetic data points that mimic the distribution of the training data. These synthetic data points can then be combined with the original dataset, effectively augmenting the training set.
We also can generate additional data points that are similar to the original dataset, thereby increasing the diversity and size of the training set. This, in turn, improves the generalization and performance of machine learning models.