How does a Generative Adversarial Network (GAN) work? Explain its two components.
How does a Generative Adversarial Network (GAN) work? Explain its two components.
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15-Apr-2025
Updated on 17-Apr-2025
Khushi Singh
17-Apr-2025Generative Adversarial Network (GAN) represents a model framework in machine learning that generates new data patterns that match existing datasets through its generative capabilities. The neural network design of GANs was introduced by Ian Goodfellow in 2014 through two components: the Generator and Discriminator that operate during adversarial training.
Here’s how a GAN works:
The role of the Generator in this model entails its ability to synthesize artificial information that mirrors genuine data points. The generator accepts random noise from latent space to generate data resembling that in the training dataset (such as artificial visual content or artificial speech or artificial writing). The generator makes artificial data that attempts to deceive the discriminator about its fabricated nature.
The discriminator functions as a detection system which determines real (from training) against fake (from generator) data entries. The model produces an answer representing the output probability that investment strategy comes from a real instance or an artificial one.
Training takes place when each model advances in parallel.
Through this process both models engage in an ongoing competition that advances their performance. Trainings proceed until the generator successfully produces fake data which enables the discriminator to distinguish real data from its fake counterparts no longer.
GANs find extensive application in image generation and deepfake creation and image-to-image translation and style transfer because they produce very realistic synthetic data.