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Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
2021
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
doi:10.1109/tpami.2021.3116668
pmid:34591756
fatcat:yjpayhmrfnaeziahmrgiyvtxkm