Adversarial Networks, specifically Generative Adversarial Networks (GANs), are a class of machine learning models that consist of two neural networks trained simultaneously through a competitive process:
- Generator: Creates new data instances, such as images or text, aiming to mimic real data.
- Discriminator: Evaluates the authenticity of the generated data, distinguishing between real and fake instances.
This adversarial setup enables GANs to produce highly realistic data outputs.
Applications/Use Cases:
- Image Generation: Creating realistic images from random noise, useful in art and design.
- Data Augmentation: Generating synthetic data to enhance training datasets, especially in scenarios with limited real data.
- Video Prediction: Forecasting future frames in a video sequence, aiding in video analysis and autonomous driving.