A Generative Adversarial Network (GAN) is a machine learning model that is used by businesses and organizations to generate new data that resembles existing data. A GAN uses comprises two neural networks, a generator and a discriminator, that operate at the same time to produce new data points.
The generator is responsible for creating new data based on the existing data provided to the GAN. The discriminator distinguishes between real data (the existing data) and fake data (the data made by the generator) and provides an estimate as to the likelihood of a particular piece of data being made up or real.
Benefits of GANs
GANs offer a number of benefits and are suitable for a wide range of applications. Below, we have covered why the use of generative adversarial networks may be beneficial for your business.
Data Augmentation
GANs can produce realistic data based on pre-existing data, which is helpful for augmenting your data sets. This enables you to increase the size and diversity of your data and generate more complex data in various forms, including text, images, and audio.
GANs can augment data of all kinds to produce new datasets or balance existing datasets. For example, if you manage a healthcare business and need realistic images to use in practice, you can use a GAN to create these images and avoid using original hospital data to protect it from cyber attacks.
If you have a set of data that has a lot more of one type of data over another, a GAN can help generate more images or audio clips that are similar to the minority dataset.
Automation
Once a GAN has been ‘trained’ to undergo specific functions, it can be left to generate more and more data without much need for human intervention. A GAN can read labelled data and organize it appropriately, automating many iterative processes.
Therefore, adopting a GAN can speed up data processing and analysis while reducing the need for manual labor.
Data Generation
The main use of a GAN is to produce new data from old data. It generates and enhances data to reduce the need for manual data production, lowering costs and the risk of errors. The time and money saved by a GAN can be invested elsewhere in your business to enhance operational efficiency and productivity.
Specific Applications of GANs
Here are some business-specific applications of generative adversarial networks.
- Natural Language Processing – GANs are ideal for generating new data in text or audio form. They are, therefore, beneficial for use in Natural Language Processing (NLP) and speech recognition software programs.
- Medical imaging – GANs are commonly used in the healthcare industry to generate medical images that can used in training to prepare healthcare professionals for the real thing. For example, they may be used to produce x-ray images when training radiologists and radiographers.
- Data privacy – GANs may be used to generate data that is similar to, but not identical to, sensitive or confidential data. This allows data to be shared while maintaining the privacy of those involved.
