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GANs are implicit generative models, [8] which means that they do not explicitly model the likelihood function nor provide a means for finding the latent variable corresponding to a given sample, unlike alternatives such as flow-based generative model. Main types of deep generative models that perform maximum likelihood estimation [9]
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [2] [3] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).
Diffusion models, generative models used to create synthetic data based on existing data, [53] were first proposed in 2015, [54] but they only became better than GANs in early 2021. [55] Latent diffusion model was published in December 2021 and became the basis for the later Stable Diffusion (August 2022).
The original GAN method is based on the GAN game, a zero-sum game with 2 players: generator and discriminator. The game is defined over a probability space (,,), The generator's strategy set is the set of all probability measures on (,), and the discriminator's strategy set is the set of measurable functions : [,].
A ganimal, also commonly referred to as GANimal, is a hybrid animal created with generative artificial intelligence systems, such as generative adversarial networks (GANs) or diffusion models. [2] [3] [4] The concept was created for a website from the MIT Media Lab in 2020, where users could create ganimal images.
Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model. Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers.
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [ 3 ] Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian ) on training images.