Generative AI, including models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), has made remarkable strides in creating realistic images and videos. These technologies are at the forefront of modern artificial intelligence, bridging the gap between creativity and technology.
Generative AI accomplishes this feat by learning from vast datasets of real-world images and videos. It then employs a two-step process to generate new content. Here’s how it works:
Generator Network: The generator network takes random noise as input and attempts to produce data that resembles real images or videos. This network is responsible for the creative aspect, introducing variations and uniqueness into the content.
Discriminator Network: Simultaneously, there’s a discriminator network that evaluates the content generated by the generator. Its role is to distinguish between real and generated content. It’s like a detective trying to spot fake art from genuine masterpieces.
These two networks engage in a continuous battle. The generator aims to produce content that fools the discriminator into believing it’s real, while the discriminator becomes increasingly skilled at telling the difference. This back-and-forth training process eventually results in the generator creating highly realistic images and videos.
