5 Applications of GANs in the Business World

While many consumers may have seen news stories in technology publications about the ability of Generative Adversarial Networks (GANs) to recreate images, photographs, and artwork in a manner that looks incredibly accurate and realistic, this flexible technology can also be used to tackle a number of other business problems. With all this being said, 5 other examples of the ways in which GANs are currently being implemented within a specific business context include text-to-image translation, denoising, enhanced product description/personalization, data augmentation, and architecture/ industrial design, as generative models are being used to support all of these business pursuits in one form or another.

Text-to-image translation

In addition to creating synthetic images in accordance with the characteristics and patterns that a GAN identifies within real images, these neural networks can also be used to create images in accordance with written text. To illustrate this point further, software developers at Cornell University were able to use a series of stacked GANs to create synthetic images depicting birds by simply writing sentences that described the physical characteristics of said birds in 2016. For example, one such sentence stated “the small bird has a red head with feathers that fade from red to gray from head to tail”, while another sentence read “the bird is black with green and has a very short break”. These sentences were then used to create synthetic images of birds during the course of two stages of development.

Denoising

Denoising is one of the many techniques that can be used to improve the visual quality of images. This being said, software developers have found that they can use GAN networks to denoise images in a very efficient way. To do this, a developer software will feed a noised image as an input to a GAN network, with the goal of creating a synthetic image that is of a higher resolution and clarity than the original image. This technique has been particularly useful in removing statistical noises that are present in medical images such as x-rays, as the minute details within such images can be the difference in saving the life of a medical patient that is suffering from a particular sickness or ailment.

Enhanced product description/personalization

In keeping with the use of GANs to recognize certain patterns within written text, another business application of this technology is in enhanced product descriptions and the personalization of the customer experience. A common example of this in the business world is in personalized emails that a business sends to their millions of customers, as well as automatic birthday notifications that many social media users will encounter when using their accounts on their birthdays. Due to the size and scope of these business emails and notifications, having an actual worker type these messages manually would prove to be impractical, and as such, GANs can be implemented to help businesses serve their customers in the most effective way possible.

Data augmentation

Irrespective of the methods or algorithms that are used, all technological products, systems, or services that are created on the basis of machine learning models will be dependent on some form of training data. To this point, software developers have also been able to train GANs to generate new data by feeding existing data into the network, with the aim of creating an expanding data set without having to sacrifice the quality of said data. Moreover, these methods can also help save software developers and business owners alike time and money, as locating and manually labeling the amount of data that is needed to create accurate machine learning models can be both an expensive and arduous undertaking.

Architecture and industrial design

Generative Adversarial Networks have also been very successful in helping businesses create 3D images for new products and services they may be looking to implement. To this end, GANs can be trained to generate 3-dimensional models after being trained on images of 2-dimensional models that have been captured from a variety of different angles and perspectives. Going back to the economic realities of running a business, this technology is very beneficial to architects and industrial designers, as creating the physical structures that these respective buildings need to operate will often be prohibitively costly, even before taking other mitigating factors into consideration, such as building materials, among other things.

Generative Adversarial Networks are truly changing the way in which business professionals from all kinds of industries approach problems they may encounter when looking to reach a certain objective. On top of this, the capabilities of generative models have also enabled people to reduce the costs associated with technological solutions that have already been proven to be productive, as creating an object within a 3D or synthetic space will almost always be cheaper than creating the same object within the physical world. Likewise, software developers will continue to discover new ways in which they can leverage GANs to create new business solutions.