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What Are GANS?

In this video, you’ll learn more about what so-called Generative Adversarial Networks are (so-called GANS), how they are created and what they could mean for the future of digital media.


What are GANs?

Generative adversarial networks (GANs) are a type of machine learning system that is made up of two parts: a generative model, which produces data samples, and a discriminative model, which tries to determine whether a given sample is real or fake. The two models are trained together in a zero-sum game, where the generative model tries to produce samples that are indistinguishable from real data, and the discriminative model tries to correctly identify fake samples. GANs have been used for a variety of tasks, including image generation, data augmentation, and semi-supervised learning.


How can fashion brands use them?

Fashion brands can use GANs in a few different ways. For example, they could use them to generate new designs for clothing or accessories or to create virtual samples of how a garment will look on a model before it is actually produced. They could also be used to augment existing data sets of clothing designs, by generating additional variations on existing designs, or by synthesizing new designs that are similar to existing ones. Additionally, fashion brands could use GANs to improve the efficiency of their production processes, by generating realistic simulations of clothing and accessories that can be used for testing and quality assurance.


Video by ColdFusion.