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

• URL: http://arxiv.org/abs/2103.04922v1
• Date: Mon, 8 Mar 2021 17:34:03 GMT
• Title: Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
• Authors: Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks
• Abstract summary: Deep generative modelling is a class of techniques that train deep neural networks to model the distribution of training samples. This compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows.
• Score: 7.477211792460795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative modelling is 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 making 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 drawn under a single cohesive framework, comparing and contrasting to explain the premises behind each, while reviewing current state-of-the-art advances and implementations.

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