What is Generative Adversarial Networks (GANs)?

Skill Level:

Generative Adversarial Networks are a type of Machine Learning model that consists of two neural networks: a generator and a discriminator. GANs are used to generate synthetic data that resembles real data by learning from training examples. They find applications in image generation, video synthesis, and data augmentation.

Other Definitions

Bayesian networks are Probabilistic Graphical Models that represent and evaluate uncertainty and conditional dependencies between variables. Industries such as healthcare and finance use Bayesian…
Multi-Modal learning refers to AI models that learn from multiple sources of data, such as text, images, and audio. By incorporating information from multiple…
Pattern Recognition is an AI technique that recognises patterns and structures in data. This approach involves identifying common features or characteristics and using these…
Federated Learning is a privacy-preserving technique where AI models are trained across multiple decentralised devices or systems without sharing raw data. Instead, only aggregated…