Generative AI

An in-depth exposition for generative adversarial networks

Generative AI has been a disruptive force that has transformed how modern businesses operate. From simple chatbots for enhanced customer experience to advanced analytical tools for optimised operational efficiency, AI tools have touched virtually every industry. Generative Adversarial Networks (GAN) is the next transformative trend that is set to accelerate the use of generative AI tools in business applications.

But what is GAN, and what are some common generative adversarial network applications?

What is a Generative Adversarial Network (GAN)?

A Generative Adversarial Network (GAN) is a unique take on machine learning algorithms and generative modelling to generate a new dataset from scratch (with zero inputs) based on the training data. Ian Goodfellow and his teammates developed GANs capable of creating text, image, video, or audio data in 2014.

For example, by combining the capabilities of deep learning and neural networks, GEN AI can create realistic human faces from scratch – that have not existed before – based on the training data. Due to their unique capabilities, GANs can be used for quality data generation, unsupervised learning, and predictive applications.

Components of GAN

The two main building blocks of GAN tools in an AI model are the generator and the discriminator, which are constantly engaged in a zero-sum “cat-and-mouse” game. The generator neural network relies on the unsupervised learning approach to take in random data (noise) and generate synthetic data. The generator aims to fool the discriminator into believing the fake data is real. The discriminator, on the other hand, takes a supervised learning approach to classify between real (from the actual dataset) and fake (from the generator).

We can compare the generator and discriminator to a forger and a detective, where the forger’s task is to create impeccable counterfeits, and the detective’s job is to identify the fakes. In this process, the discriminator offers feedback to the generator, allowing it to improve its data generation capabilities. The training stops when the discriminator is unable to tell the difference between real and generated data, and a generalised GAN model is created.

Applications of generative adversarial networks

The initial aim behind developing generative adversarial networks was to stop machine learning and neural network algorithms from being fooled into misclassifying data with some noise. However, since their development, GANs have found applications across many domains, including–

  • Generate new data based on the available datasets
  • Generate realistic pictures of people
  • Generate music using voice clone
  • Generate images using text
  • Create characters for video games and animated productions
  • Translate one image into another by altering external characteristics
  • Create a high-resolution version from a low-resolution image or video
  • Predict the next frame in a video
  • Generate 3D objects for VR and gaming applications
  • Apply style transfer between images

Benefits and limitations of generative adversarial networks

With its wide range of applications, generative adversarial networks offer several benefits when it comes to advancing machine learning algorithms. Some of the major benefits of using GAN include:

  • GANs facilitate high-quality data generation
  • GANs make unsupervised learning – from unlabelled data – possible
  • GANs are versatile and have cross-domain applications, including text-to-image translation, image-to-image translation, and domain transfer
  • GANs offer improved performance efficiency compared to other generative models

Despite their applications and potential, GAN AI is not without challenges. The debate on the ethics of using AI extensively has not been able to keep up with the technological developments in AI. We have yet to discern the technical and social nuances of its ethical use. Some of the key technical limitations of GANs include:

  • GANs are hard to train
  • Generator training may fail if the discriminator is too good and does not provide enough feedback information for the generator to improve
  • GAN model parameters may oscillate and fail to converge
cMoreover, unrestricted use of generative adversarial network applications can lead to scams using deep fake, which would require robust regulatory frameworks to mitigate malicious use and negative effects of GAN-generated content.

How can Infosys BPM help?

Infosys BPM offers a generative AI platform for businesses that can help them transform their business operations and harness the power of AI to stay competitive. With AI for Business solutions, you can generate data to augment and enhance your business operations and gain valuable insights to accelerate value creation.
Whether you want to re-imagine your business operations with generative AI, drive AI-first digital transformation, or reinforce AI ethics, Infosys BPM can help you leverage applications of GANs to lead the generative evolution.

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