Generative AI – TV Geetha

GANs unique way of training a generative model – framing the problem as a supervised learning problem with two components namely the generator model trained to generate new examples, and the discriminator model classifying examples as either real or newly generated. Let us illustrate this concept using the example given by GANs’ founder Ian Goodfellow.

We assume that we have a counterfeiter trying to generate fake money (generator) while the police (discriminator) try to distinguish between real and fake money. Initially the generated counterfeit is easily distinguishable but as experienced is gained, the counterfeit money becomes almost indistinguishable from the real money (Figure 6.1). Figure 6.1 Example for the Concept of GANs Credit: Amitali / Shutterstock The generation of increasingly realistic and coherent data is driven by the competition between the generator and the discriminator.

In this adversarial net framework, the generative model that given noise learns the real data distribution to generate fake samples has to tackle an adversary. The adversary is the discriminative model that learns to determine whether a sample is from the real model distribution or is fake. The discriminator provides confidence (probability p) of a sample being real that is from training data.

With more experience, the generator becomes a better expert at creating realistic data, while the discriminator becomes an expert at detecting fake data (Figure 6.2). Figure 6.2 Generative Adversarial Network 6.1.1 GANs – Supervised or Unsupervised Learning? As discussed, GAN uses two neural networks namely generator and discriminator. The generator network generates an image from random noise (not provided training data) and hence and is a type of unsupervised machine learning. The discriminator network classifies an image as real or fake when provided with data known to be real or fake which are used to train the models and hence is a kind of supervised learning.

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