Generative Adversarial Networks (GANs) | Estateplanning

Generative Adversarial Networks (GANs) are a type of deep learning model that uses a two-player game framework to generate new, synthetic data that resembles existing data, with applications in image and video generation, natural language processing, and more. Researchers like Andrew Ng and Fei-Fei Li have explored GANs for various tasks, while companies like Google and Facebook have utilized GANs for tasks such as image generation and data augmentation. The concept of GANs has also been influenced by the work of David Rumelhart and Geoffrey Hinton, who have made significant contributions to the field of deep learning.