Generative Adversarial Network (GAN)
A GAN is a machine learning framework where two neural networks are trained simultaneously in a competitive setting:
- The generator tries to produce synthetic samples (e.g., images).
- The discriminator attempts to distinguish between real and generated samples.
Over time, the generator becomes better at producing data that looks indistinguishable from the real distribution.
Real-world use cases
- Art & creativity: producing AI-generated paintings and music.
- Data augmentation: generating synthetic datasets when real data is scarce.
- Fashion & retail: creating virtual clothing prototypes.
- Robotics: simulating sensor data for autonomous systems.
Limitations
- Training can be computationally expensive.
- Risk of overfitting to training distribution.
- Susceptible to ethical misuse (fake media, misinformation).
One of the most fascinating aspects of GANs is their ability to capture complex, high-dimensional distributions without explicit probability modeling. This makes them powerful tools for unsupervised learning, as the generator essentially learns the underlying structure of data by continuously being “challenged” by the discriminator.
Variants of GANs have been developed to address different challenges. Conditional GANs (cGANs) allow the generation of data guided by specific labels (e.g., generating images of shoes of a given color). CycleGANs make it possible to translate images between domains without paired examples, such as converting summer landscapes into winter ones. StyleGANs, on the other hand, have become famous for producing highly realistic human faces.
Despite their strengths, GANs are notoriously difficult to train. Issues such as mode collapse, where the generator produces limited varieties of outputs, or unstable convergence, remain active areas of research. Techniques like Wasserstein GANs (WGANs) or spectral normalization have been introduced to improve training stability and diversity of generated samples.
Beyond entertainment and research, GANs also hold promise in critical fields. In healthcare, they can generate synthetic medical images that preserve patient privacy while enabling AI model training. In cybersecurity, GANs are studied for their potential to simulate attack scenarios and strengthen defenses. These diverse applications illustrate the versatility of GANs but also highlight the responsibility of researchers and practitioners to ensure their ethical use.
📚 Further reading:
- Ian Goodfellow’s original paper (2014).
- O’Reilly, Deep Learning for Vision Systems (2020).
