Generative Adversarial Network
Artificial intelligence (AI) and machine learning tools are evolving at a rapid pace, and generative adversarial networks (GANs) are emerging as a valuable resource in the quest to take these technologies to the next level. Applications and uses of generative adversarial networks include data generation and augmentation, image synthesis, art creation, and a lot more. Discover how generative adversarial networks work, their applications across various domains, and the role they are playing in shaping the future of AI.
What is a generative adversarial network?
Generative adversarial networks (GANs) are a class of artificial intelligence (AI) frameworks designed to produce data samples based on a provided training dataset. GANs are primarily used in the training of unsupervised machine learning models. A generative adversarial network is comprised of a pair of competing neural networks, namely a generator and a discriminator. The generator creates synthetic data samples resembling those in the training dataset, and the discriminator attempts to distinguish between authentic data samples and those created by the generator. The ultimate goal of a generative adversarial network is to produce artificial data samples that are realistic enough not to be flagged as fake by the discriminator.
What are the primary components of a generative adversarial network?
There are two key components to any generative adversarial network. These are the generator and the discriminator. The generator is responsible for the creation of new synthetic or fake data based on a training dataset. This data may be in the form of images, audio, or text. On the flip side, the discriminator is responsible for accurately identifying authentic and fake data and telling them apart. These two components are perpetually in a competitive learning process, which leads to the generation of increasingly realistic synthetic data.
How do generative adversarial networks work?
The two components of a generative adversarial network are constantly in competition to outdo the other. The generator produces fake data samples that mimic samples taken from the training data in an attempt to trick the discriminator into believing that these fake data samples are authentic. The discriminator, on the other hand, is trained to spot and flag synthetic data that has been created by the generator and classifies data samples as authentic or bogus. Based on feedback from the discriminator, the generator continually improves its ability to generate realistic data, ultimately making it harder for the discriminator to tell synthetic data from the real thing.
Generative adversarial networks are capable of completing diverse tasks, including the creation of text, images, and videos. So far, it has demonstrated its ability to produce compelling depictions of animals, human faces, and landscapes.
What are the popular applications of generative adversarial networks?
Generative adversarial networks have a number of applications in the area of unsupervised learning. GANs can be used for data augmentation in circumstances where data is scarce, text and image generation, and more. The ability to produce accurate images of human faces has been used to train facial recognition algorithms, while synthetic medical images generated by generative adversarial networks have been used in the healthcare industry to train machine learning models to detect maladies more accurately.
Are there any challenges when working with generative adversarial networks?
Working with an emerging technology like generative adversarial networks does come with certain challenges and difficulties. They are known to be unstable and, if not appropriately configured, can throw up undesirable results. They also need massive resources to run and, hence, aren’t viable in a number of applications. Interpreting the data generated by GANs is another challenge, which makes it difficult for users to understand the root cause behind why certain results have been created.
The synthetic data created by generative adversarial networks can, in this manner, be used for the training of machine learning models across various sectors, the refinement of 3D printer images, and even the creation of art and new pharmaceutical molecules. With a growing number of uses and applications, Generative AI for BPM is set to take today’s artificial intelligence and machine learning technologies to a whole new level.
