Generative Adversarial Network (GAN) is a type of machine learning that consists of two neural networks: one that generates data and one that discriminates and refines the data. The two neural networks compete to create more accurate predictions.
Current Relevance
While GANs were foundational in generative AI, newer models like diffusion models (for high-fidelity image generation) and transformers (for text and multimodal tasks) now lead in many domains.
Still, GANs remain valuable for:
- Efficiency – fast inference for real-time use;
- Low compute needs – ideal in constrained environments;
- Specialized tasks – like data augmentation and anomaly detection with limited data.