📌 Generative Adversarial Networks (GANs) Summary
Generative Adversarial Networks, or GANs, are a type of artificial intelligence where two neural networks compete to improve each other’s performance. One network creates new data, such as images or sounds, while the other tries to detect if the data is real or fake. This competition helps both networks get better, resulting in highly realistic generated content. GANs are widely used for creating images, videos, and other media that are hard to distinguish from real ones.
🙋🏻♂️ Explain Generative Adversarial Networks (GANs) Simply
Imagine a game between a skilled artist and a sharp-eyed judge. The artist tries to create fake paintings that look real, and the judge tries to spot the fakes. As they compete, the artist gets better at creating convincing fakes, and the judge gets better at spotting them. Over time, this back-and-forth makes both of them improve.
📅 How Can it be used?
GANs can generate realistic synthetic photos for use in advertising when there are not enough real images available.
🗺️ Real World Examples
A company can use GANs to create lifelike images of clothing on models for online shops, even if those exact photos were never taken. This allows them to show products in different colours or styles without organising new photoshoots.
Researchers use GANs to enhance old, low-resolution photos by generating high-resolution versions that restore details, making it possible to improve the quality of historic images or family portraits.
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