Learn about Generative Adversarial Networks (GANs) and how they work through an example of generating realistic flower images. Prepare for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
A generator creates an image of a flower and the discriminator determines if the image is real or fake. This process is repeated until the generator is able to create a image of a flower good enough to fool the discriminator.
This is an illustration of which of the following machine learning models?
A. Generative adversarial network (GAN)
B. Natural language processing (NLP)
C. Convolutional neural network (CNN)
D. Heads up guidance system (HGS)
Answer
A. Generative adversarial network (GAN)
Explanation
It’s an example of a generative adversarial network (GAN). This GAN has two convolutional neural networks (CNNs). One CNN, the generator, is working towards creating output that is good enough to fool the other CNN, called the discriminator.
The scenario described in the question, where a generator creates an image of a flower and a discriminator determines if the image is real or fake, with the process repeating until the generator can create an image good enough to fool the discriminator, is an illustration of a Generative Adversarial Network (GAN).
A Generative Adversarial Network (GAN) is a type of machine learning model that consists of two neural networks – a generator and a discriminator – that compete against each other in a game-like setup. The generator tries to create realistic data (in this case, images of flowers) with the goal of fooling the discriminator into thinking the generated data is real. Meanwhile, the discriminator is trained to distinguish between real data and the fake data produced by the generator.
During training, the generator and discriminator are locked in a constant back-and-forth:
- The generator creates fake data and sends it to the discriminator.
- The discriminator evaluates the data and provides feedback on how realistic it is.
- The generator uses this feedback to adjust its parameters and create more realistic fake data.
- The process repeats, with both networks improving over time.
Eventually, through many iterations of this adversarial process, the generator becomes skilled at creating highly realistic fake data that the discriminator can no longer distinguish from real data. At this point, the GAN has effectively learned to generate new data that mimics the characteristics of the real data it was trained on.
GANs have been used for a variety of applications, including generating realistic images, videos, and audio, as well as for data augmentation, style transfer, and anomaly detection. They are a powerful tool for generative modeling and have produced some of the most impressive results in AI generated content.
Therefore, the correct answer to the question is A. Generative adversarial network (GAN). The other options – NLP, CNN, and HGS – are different types of machine learning models or systems that do not match the specific example provided.
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