Creating Artificial Realities: An Overview of Generative Adversarial Network (GAN)

btd
3 min readNov 14, 2023

A Generative Adversarial Network (GAN) is a type of artificial intelligence algorithm used in unsupervised machine learning. GANs were introduced by Ian Goodfellow and his colleagues in 2014 and have since become a significant advancement in the field of generative modeling. Here’s a comprehensive overview of GANs:

1. Objective:

  • GANs are designed to generate new, realistic data samples that resemble a given dataset. The model consists of two neural networks, a generator, and a discriminator, which are trained simultaneously through adversarial training.

2. Architecture:

Generator:

  • The generator takes random noise as input and transforms it into synthetic data samples.
  • It starts with random noise and gradually refines its output to resemble real data.
  • Typically consists of transposed convolutional layers to upsample the data.

Discriminator:

  • The discriminator evaluates whether a given input is real (from the actual dataset) or fake (generated by the generator).
  • It is trained to improve its ability to distinguish between real and fake samples.

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