Generative Adversarial Network Training

Generative adversarial networks (GANs) are a deep learning framework aiming to generate realistic synthetic data by pitting two neural networks—a generator and a discriminator—against each other in a competitive training process. Current research focuses on improving GAN training stability and robustness, exploring various architectures like convolutional GANs and incorporating techniques such as score matching and conditional inputs to enhance data quality and control. The ability of GANs to generate high-fidelity synthetic data is proving valuable across diverse fields, from augmenting limited datasets for improved model training in areas like gravitational wave detection and mmWave sensing to creating realistic 3D avatars and accelerating simulations of complex physical phenomena like turbulence.