Generative adversarial networks, or GANs, are a powerful type of neural network used for unsupervised machine learning. Made up of two competing models which run in competition with one another, GANs are able to capture and copy variations within a dataset.
They’re great for image manipulation and generation, but they can also be deployed for tasks like understanding risk and recovery in healthcare and pharmacology.
GANs are actually pretty new – they were first introduced by Ian Goodfellow in 2014. Goodfellow developed them to tackle some of the issues with similar neural networks, including the Boltzmann machine and autoencoders.
Both the Boltzmann machine and autoencoders use the Markov Decision Chain which has a pretty high computational cost. This efficiency gives engineers significant gains – which you need if you’re working at the cutting edge of artificial intelligence.
How do Generative Adversarial Networks work?
Let’s start with a simple analogy. You have a painting – say the Mona Lisa – and we have a master forger who wants to create a duplicate painting. The forger does this by learning how the original painter – Leonardo Da Vinci – produced the painting.
Meanwhile, you have an investigator trying to capture the forger and ‘second guess’ the rules the forger is learning.
To map this onto the architecture of a GAN, the forger is the generator network, which learns the distribution of classes while the investigator is the discriminator network, which learning the boundaries between those classes – the formal ‘shape’ of the dataset.
Applications of GANs
Generative adversarial networks are used for a number of different applications.
One of the best examples is a Google Brain project back in 2016 – researchers used GANs to develop a method of encryption.
This project used 3 neural networks – Alice, Bob, and Eve. Alice’s job was to send an encrypted message to Bob. Bob’s job was to decode that message, while Eve’s job was to intercept it.
To begin with Alice’s messages were easily intercepted by Eve. However, thanks to Eve’s adversarial work, Alice began to develop its own encryption strategy – it took 15,000 runs for Alice to successfully encrypt a message that could be deciphered by Bob that Eve couldn’t intercept.
Elsewhere, GANs are also being used in fields such as drug research. The neural networks can be trained on the existing drugs and suggest new synthetic chemical structures that improve on drugs that already exist.
Generative adversarial networks: the cutting edge of artificial intelligence
As we’ve seen, GANs offer some really exciting opportunities in artificial intelligence. There are two key advantages you need to remember: GANs solve the problem of generating data when you don’t have enough to begin with and they require no human supervision.
This is crucial when you think about the cutting edge of artificial intelligence, both in terms of the efficiency of running the models, and the real-world data we want to use – which could be poor quality or have privacy and confidentiality issues, as much healthcare data does.