GAN synthesis refers to the process of leveraging a generative adversarial network (GAN) to generate additional training data. By training two neural networks— a generator that creates synthetic data and a discriminator that distinguishes real data from fake data— GANs can produce highly realistic samples that mimic the characteristics of the original dataset, thereby augmenting the quantity and diversity of training materials.
