Image Generation

Image Generation can be divided into four categories:

  • Variational Autoencoders (VAEs). Variational autoencoders are “probabilistic generative models that require neural networks as only a part of their overall structure”. In operational words, they encode images to a compressed size and decode them to the original size. During this process, they learn the distribution of the data.
  • Generative Adversarial Models (GANs). These are generally the most known, at least as a word that resonates in the field of generative AI. A GAN is “a class of ML framework in which two Neural Networks are pith against each other where the gain of one is the loss of the other”. This means that one Neural Network creates the image while the other predicts if it is real or fake.
  • Autoregressive models. In statistics, an autoregressive model is the representation of a random process. In the context of generative images, these kinds of models generate images by treating images as a sequence of pixels.
  • Knowledge Base/Artificial Intelligence/Machine Learning/Machine Learning Concepts/Diffusion Models. Diffusion models have been inspired by thermodynamics and are definitely the most promising and interesting kinds of models in the subfield of image generation.