“Generative Adversarial Networks (GANs), are a class of artificial intelligence algorithms used in machine learning.”
GANs are composed of two neural networks, the generator, and the discriminator, that work in opposition to each other.
The generator network generates synthetic data, such as images or text, from random noise. The discriminator network evaluates the generated data and real data to distinguish between them. This adversarial relationship compels both networks to improve continuously, with the generator striving to create more convincing data, and the discriminator becoming better at distinguishing real from fake.
Advantages of GANs:
- Can generate high-quality, realistic-looking images
- Potentially can learn from data with no label information for unsupervised learning tasks
- An easy and quick training process
GANs have revolutionized the field of generative modeling, innovating applications in art, entertainment, data enhancement, and more. Their adversarial nature drives continuous improvement, making GANs a powerful tool for data generation and manipulation.