A comprehensive survey and analysis of generative models in machine learning

By GM Harshvardhan, M. Gourisaria, M. Pandey, Siddharth Swarup Rautaray. Computer Science Review, 2020

454 citations recorded by the source corpus.

Abstract Generative models have been in existence for many decades. In the field of machine learning, we come across many scenarios when directly learning a target is intractable through discriminative models, and in such cases the joint distribution of the target and the training data is approximated and generated. These generative models help us better represent or model a set of data by generating data in the form of Markov chains or simply employing a generative iterative process to do the same. With the recent innovation of Generative Adversarial Networks (GANs), it is now possible to make use of AI to generate pieces of art, music, etc. with a high extent of realism. In this paper, we review and analyse critically all the generative models, namely Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Latent Dirichlet Allocation (LDA), Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Deep Boltzmann Machines (DBM), and GANs. We study their algorithms and implement each of the models to provide the reader some insights on which generative model to pick from while dealing with a problem. We also provide some noteworthy contributions done in the past to these models from the literature.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8509cc4e5421db6fdfd30528bcc15c651be0c0fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Generative artificial intelligence (2023, methodology): “Generative models have been around for decades, with, for example, hidden Markov models or Bayesian networks aiming to model statistical problems involving time series or sequences (Gm et al., 2020).”