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• W3204937802 endingPage "7347" @default.
• W3204937802 startingPage "7327" @default.
• W3204937802 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." @default.
• W3204937802 created "2021-10-11" @default.
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• W3204937802 date "2022-11-01" @default.
• W3204937802 modified "2023-10-18" @default.
• W3204937802 title "Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models" @default.
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• W3204937802 doi "https://doi.org/10.1109/tpami.2021.3116668" @default.
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• W3204937802 hasPublicationYear "2022" @default.
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