Generative Adversarial Networks, or GANs, are a type of generative AI where two neural networks compete to create realistic data.
- Generator: Creates new data samples
- Discriminator: Evaluates and distinguishes real vs generated data
This adversarial process improves the quality of generated outputs over time.
How GANs Work
- Generator produces data: Tries to mimic real data.
- Discriminator evaluates: Determines if the data is real or fake.
- Feedback loop: Both networks improve—generator becomes better at producing realistic data, discriminator becomes better at detecting fakes.
- Iteration: Process repeats until generated outputs are convincing.
Advantages of GANs
- Generates highly realistic images, audio, and video
- Useful for data augmentation in training ML models
- Can simulate scenarios for research or design
- Creative applications in art, gaming, and media
Disadvantages
- Training can be unstable and difficult
- Requires large datasets and computing power
- Outputs may inherit biases from training data
Real-World Examples
- Deepfake videos and images
- AI-generated artwork
- Synthetic training data for ML models
- Game environment creation
- Fashion and design simulations
Conclusion
GANs are powerful AI tools that generate realistic content through competition between networks, unlocking creativity and innovation in multiple fields.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/