What Are GANs?
Generative Adversarial Networks (GANs) are two Artificial Intelligence systems which learn by competing against each other. This makes them one of the most interesting and powerful AI techniques.
How GANs Work
GANs work by having two AI networks compete directly against each other. The first is the Generator, this AI networks job is to create new data which is indistinguishable from real-world data. Secondly there is the Discriminator which examines the data created by the Generator, and tries to distinguish between real-world data and the data created by the Generator.
The Generator and Discriminator work in tandem, trying to outsmart each other. This creates a dynamic environment similar to natural selection, where only the strongest AI networks survive.
Advantages of GANs
GANs have many advantages for AI research, some of the most notable are listed below:
- Data Generation: GANs can be used to generate large amounts of synthetic data which can be used to train other AI networks.
- Unsupervised Learning: GANs are capable of learning through indirect supervision, allowing them to learn new tasks with little to no direct supervision.
- Realistic Output: GANs can generate incredibly realistic output which is incredibly difficult for humans to differentiate from real-world data.
Conclusion
GANs are one of the most promising AI techniques in existence, they are capable of producing large amounts of realistic data and can learn through indirect supervision. While they are still in their infancy, GANs are sure to drive innovation in the AI field for many years to come.