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The first architecture we explore is Generative Adversarial Networks (GANs). This type of architecture is widely used to generate new images. This architecture was introduced in 2014 by Ian Goodfellow, and since then it has grown in popularity. What makes GANs interesting is their capacity to produce data that is similar but not equal to the original data. The synthetic data generated is incredibly realistic, which makes GANs the ideal architecture for a wide range of uses. The best is when GANs are used to generate new images. This chapter explores GANs and shows you how to write your own GANs using Python and Julia.

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Generative Adversarial Networks (GANs)

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  • 发表日期：

    2026年

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  • Pierluigi Riti

摘要

The first architecture we explore is Generative Adversarial Networks (GANs). This type of architecture is widely used to generate new images. This architecture was introduced in 2014 by Ian Goodfellow, and since then it has grown in popularity. What makes GANs interesting is their capacity to produce data that is similar but not equal to the original data. The synthetic data generated is incredibly realistic, which makes GANs the ideal architecture for a wide range of uses. The best is when GANs are used to generate new images. This chapter explores GANs and shows you how to write your own GANs using Python and Julia.