Generative Adversarial Networks (GANs) are an AI framework using two competing neural networks a Generator and a Discriminator to create realistic new data (images, text, etc.) that mimics a training dataset, with the Generator learning to fool the Discriminator, and the Discriminator learning to spot fakes, constantly improving until the generated data is indistinguishable from real data.
GANs consist of two neural networks: > Generator: Creates fake data > Discriminator: Identifies real vs fake data They compete until the output becomes highly realistic.
GANs can learn complex patterns without labeled data, making them highly effective for creative and data-scarce applications.
> Image and video generation > Face synthesis and deepfakes > Data augmentation > Art and design automation
Used across healthcare, gaming, fashion, marketing, and media for simulation, personalization, and visual innovation.
GANs are shaping the next generation of generative AI, driving realism, creativity, and automation across digital experiences.