Generative Adversarial Networks (GANs) are a class of deep learning algorithms used to generate new data that resembles existing data. Proposed by Ian Goodfellow in 2014, GANs frame the problem as a game between two neural networks: the generator and the discriminator.
How GANs Work
GANs operate through a competitive process:
- 🖼️ Generator: Creates fake data (e.g., images) from random noise.
- 🔍 Discriminator: Judges whether the data is real or fake.
This adversarial training enables the generator to produce increasingly realistic outputs.
Key Applications
GANs are widely applied in:
- 🎨 Image synthesis (e.g., creating art, faces)
- 🔄 Style transfer and image editing
- 🧪 Data augmentation for machine learning
- 📈 Anomaly detection in datasets
For a deeper dive into GAN architectures, visit our GAN Advanced Tutorial.
Advantages & Challenges
✅ Pros:
- High-quality generated samples
- No need for labeled data
⚠️ Cons:
- Training instability (mode collapse, vanishing gradients)
- Difficult to debug and tune