The Rise of the Battling Bots: GANs and AI Artistry

Have you ever wondered how computers can create images that look like they were made by humans?

Its actually the result of two AIs fighting with each other. It's all thanks to a branch of artificial intelligence called "generative models." In this video, we'll explore how these models work and how they're used to generate realistic images.

Be sure to like, subscribe and smash that bell for more data driven updates All the Imagines in this video are created by AI from Stablefusion.web At the core of generative models are artificial neural networks. Think of them as mathematical representations of the brain's neurons.

They consist of layers of nodes, each node performing a simple mathematical function, such as multiplying its input by a weight and adding a bias. When more neural layers are added, its called “going for the deep” or DEEP LEARNING.

To generate images, we use a type of neural network called a "generative adversarial network," or GAN for short. GANs consist of two neural networks: a generator and a discriminator.

The generator's job is to create images that look as realistic as possible, while the discriminator's job is to distinguish between the generator's images and real images. During training, the generator and discriminator are pitted against each other in a competition.

The generator creates an image and the discriminator determines if it's real or fake. The feedback from the discriminator is used to adjust the generator's weights and biases, allowing it to create more realistic images over time.

Once the generator has been trained, it can be used to create images on its own. By inputting random noise into the generator, it will create an image that has similar characteristics to the training data it was trained on. While GANs have been used to generate impressive images, they do have some limitations.

For example, they can struggle to generate fine details, such as hair or fur, and they may also produce images that lack diversity. Despite these limitations, GANs have opened up new possibilities for image generation and have been used in fields such as art, fashion, and video game design.

Who knows what other amazing applications they'll be used for in the future? Thanks for watching! If you have any questions about AI image generation, feel free to leave them in the comments below.