mlsanityData to 7 Oct 2026

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

arXiv 1606.00709 ↗First posted 2 Jun 2016stat.ML cs.LG stat.MEcited by 504

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Cited by 32 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generative-adversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f-divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models.

Key facts. Cited by 504 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2016.

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Citing papers per year: 2018: 1; 2019: 5; 2020: 6; 2021: 131; 2022: 86; 2023: 99; 2024: 69; 2025: 66; 2026: 41.

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