Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models.
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| Title: | Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. |
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| Authors: | Bond-Taylor, Sam1 samuel.e.bond-taylor@durham.ac.uk, Leach, Adam1 adam.leach@durham.ac.uk, Long, Yang1 yang.long@durham.ac.uk, Willcocks, Chris G.1 christopher.g.willcocks@durham.ac.uk |
| Source: | IEEE Transactions on Pattern Analysis & Machine Intelligence. Nov2022, Vol. 44 Issue 11, p7327-7347. 21p. |
| Subjects: | Artificial neural networks, Autoregressive models, Probabilistic generative models, Generative adversarial networks |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 160650648 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TPAMI.2021.3116668 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 7327 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Autoregressive models Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Generative adversarial networks Type: general Titles: – TitleFull: Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bond-Taylor, Sam – PersonEntity: Name: NameFull: Leach, Adam – PersonEntity: Name: NameFull: Long, Yang – PersonEntity: Name: NameFull: Willcocks, Chris G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 01628828 Numbering: – Type: volume Value: 44 – Type: issue Value: 11 Titles: – TitleFull: IEEE Transactions on Pattern Analysis & Machine Intelligence Type: main |
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