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

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,...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 11; s. 7327 - 7347
Hlavní autori: Bond-Taylor, Sam, Leach, Adam, Long, Yang, Willcocks, Chris G.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Analytical models
Artificial neural networks
Autoregressive models
Computational modeling
Data models
Deep learning
energy-based models
Generative adversarial networks
generative models
Neural Networks, Computer
Neurons
normalizing flows
Predictive models
State-of-the-art reviews
Training
variational autoencoders
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3116668