Generative Adversarial Networks, or GANs, have emerged as a groundbreaking concept in the realm of Generative AI. These networks consist of two primary components: a generator and a discriminator, which work in tandem to create and evaluate content. Here’s how GANs are used in AI:
Application | Description |
Image Generation | Create high-quality images, artworks, and more. |
Data Augmentation | Generate additional data to enhance training datasets. |
Style Transfer | Transform the style of images, e.g., artist-inspired styles. |
Super-Resolution | Enhance image resolution for clarity and detail. |
Anomaly Detection | Identify deviations from the normal data distribution. |
Text-to-Image Generation | Generate images from textual descriptions. |
