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Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Bond-Taylor, Sam; Leach, Adam; Long, Yang; Willcocks, Chris G.

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Thumbnail


Authors

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Sam Bond-Taylor samuel.e.bond-taylor@durham.ac.uk
PGR Student Doctor of Philosophy

Adam Leach adam.leach@durham.ac.uk
PGR Student Doctor of Philosophy



Abstract

Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversity, and architectural restrictions. In particular, this compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, in addition to numerous hybrid approaches. These techniques are compared and contrasted, explaining the premises behind each and how they are interrelated, while reviewing current state-of-the-art advances and implementations.

Citation

Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021). Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/tpami.2021.3116668

Journal Article Type Article
Acceptance Date Sep 22, 2021
Online Publication Date Sep 30, 2021
Publication Date 2021-11
Deposit Date Oct 29, 2021
Publicly Available Date Dec 16, 2022
Journal IEEE Transactions on Pattern Analysis and Machine Intelligence
Print ISSN 0162-8828
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 44
Issue 11
Pages 7327-7347
DOI https://doi.org/10.1109/tpami.2021.3116668

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