mlsanityData to 7 Oct 2026

Density estimation using Real NVP

arXiv 1605.08803 ↗First posted 27 May 2016cs.LG cs.AI cs.NEcited by 2,029

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Cited by 255 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations.

Key facts. Cited by 2,029 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2016.

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Citing papers per year: 2019: 1; 2020: 4; 2021: 341; 2022: 369; 2023: 395; 2024: 334; 2025: 309; 2026: 276.

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