A Wasserstein Generative Adversarial Network (WGAN) is a type of artificial intelligence model designed to generate realistic data, such as images or text. It improves upon traditional Generative Adversarial Networks (GANs) by using a different method to measure the difference between real and generated data, leading to more stable training and better-quality outputs.
Applications/Use Cases:
- Image Generation: Creating realistic images from random noise.
- Data Augmentation: Generating synthetic data to enhance training datasets.
- Art Creation: Assisting artists in generating new visual content.