What are Generative Adversarial Networks (GANs)?

Generative adversarial networks, or GANs, are a powerful type of neural network used for unsupervised machine learning. They are made up of two competing models that run in competition with one another. This unique approach allows GANs to capture and copy variations within a dataset.

GAN Applications

GANs excel in image manipulation and generation. However, their applications extend far beyond this, including understanding risk and recovery in healthcare and pharmacology.

A Brief History of GANs

GANs are a relatively new technology, first introduced by Ian Goodfellow in 2014. Goodfellow developed them to address some of the limitations found in similar neural networks like the Boltzmann machine and autoencoders. These earlier methods used the Markov decision chain, which had a high computational cost. GANs offer significant efficiency gains, a crucial factor when working at the forefront of artificial intelligence.

How GANs Work: The Forger and the Investigator

Let's illustrate the workings of a GAN with an analogy. Imagine a master forger aiming to create a duplicate of the Mona Lisa. The forger studies and learns how Leonardo DaVinci produced the original painting. Simultaneously, an investigator attempts to catch the forger by anticipating and understanding the forger's learning process.

Mapping the Analogy to GAN Architecture

In the GAN framework, the forger represents the generator network. This network learns the distribution of classes within the data. The investigator corresponds to the discriminator network, which learns the boundaries between those classes and understands the formal shape of the dataset.

RealWorld Applications of GANs

GANs have diverse applications across various fields. A notable example is a Google Brain project from 2016, where researchers used GANs to develop an encryption method.

Alice, Bob, and Eve: A GAN Encryption Experiment

This project involved three neural networks: Alice, Bob, and Eve. Alice was tasked with sending an encrypted message to Bob. Bob's role was to decode the message, while Eve attempted to intercept it. Initially, Eve easily intercepted Alice's messages. However, driven by Eve's adversarial attempts, Alice evolved its own encryption strategy. After 15,000 iterations, Alice successfully encrypted a message that Bob could decipher but Eve could not intercept. This showcases the power of adversarial learning in developing robust solutions.

GANs in Drug Research

GANs are also making strides in drug research. Neural networks can be trained on existing drugs to suggest new synthetic chemical structures that improve upon existing medications.

Key Advantages of GANs

GANs present significant opportunities in artificial intelligence. Two key advantages stand out:

• Data Generation: GANs solve the problem of generating data, especially when the initial dataset is insufficient.
• Unsupervised Learning: They require no human supervision, which is crucial in the cuttingedge areas of AI.

These advantages are vital for both the efficiency of running the models and handling realworld data that might be of poor quality or contain privacy and confidentiality issues, as often seen in healthcare data.

Learn More

To delve deeper into GANs, Packt offers a wide selection of ebooks and videos on deep learning and neural networks. Visit their website to discover more.