While Large Language Models (LLMs) are a prominent example of Generative AI, they're not the only ones. Here are some other notable types:
1. Generative Adversarial Networks (GANs)
* How they work: GANs consist of two neural networks: a generator that creates new data, and a discriminator that evaluates its authenticity. They compete, improving each other over time.
* Applications: Image generation, style transfer, and creating realistic synthetic data.
2. Variational Autoencoders (VAEs)
* How they work: VAEs are a type of neural network that learns a latent representation of data. They can generate new data points that are similar to the training data.
* Applications: Image generation, data imputation, and anomaly detection.
3. Diffusion Models
* How they work: Diffusion models gradually add noise to data and then learn to reverse the process. This can be used to generate new data points.
* Applications: Image generation, text-to-image generation, and audio synthesis.
4. Flow-based Models
* How they work: Flow-based models learn a sequence of invertible transformations that can map data to and from a simple distribution. This can be used to generate new data points.
* Applications: Image generation, density estimation, and anomaly detection.
5. Neural Style Transfer
* How it works: This technique combines the content of one image with the style of another using neural networks.
* Applications: Artistic creation, image editing, and video effects.
