# Generative Adversarial Nets from a Density Ratio Estimation Perspective

arXiv: https://arxiv.org/abs/1610.02920 · HTML: https://mlsanity.com/paper/1610.02920/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2016.

## Built on by (most cited first, 24 of 24)

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- [Meta-Learning for Relative Density-Ratio Estimation](https://mlsanity.com/paper/2107.00801/) (2021)
- [Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse](https://mlsanity.com/paper/2207.09535/) (2022)
- [A Unified Framework for Multi-distribution Density Ratio Estimation](https://mlsanity.com/paper/2112.03440/) (2021)
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- [F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits](https://mlsanity.com/paper/2110.04253/) (2021)
- [Continual Density Ratio Estimation in an Online Setting](https://mlsanity.com/paper/2103.05276/) (2021)
- [Improving Bridge estimators via f-GAN](https://mlsanity.com/paper/2106.07462/) (2021)
- [Bounds on Lp errors in density ratio estimation via f-divergence loss functions](https://mlsanity.com/paper/2410.01516/) (2024)
- [Improving Discriminator Guidance in Diffusion Models](https://mlsanity.com/paper/2503.16117/) (2025)
- [A Unified f-divergence Framework Generalizing VAE and GAN](https://mlsanity.com/paper/2205.05214/) (2022)
- [Generalized Balancing Weights via Deep Neural Networks](https://mlsanity.com/paper/2211.07533/) (2022)
- [Alpha-divergence loss function for neural density ratio estimation](https://mlsanity.com/paper/2402.02041/) (2024)
- [Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics](https://mlsanity.com/paper/2201.13127/) (2022)
- [A density ratio framework for evaluating the utility of synthetic data](https://mlsanity.com/paper/2408.13167/) (2024)
- [Boosting Statistic Learning with Synthetic Data from Pretrained Large Models](https://mlsanity.com/paper/2505.04992/) (2025)
- [Discriminative classification with generative features: bridging Naive Bayes and logistic regression](https://mlsanity.com/paper/2512.01097/) (2025)

## Abstract (arXiv)

Generative adversarial networks (GANs) are successful deep generative models. GANs are based on a two-player minimax game. However, the objective function derived in the original motivation is changed to obtain stronger gradients when learning the generator. We propose a novel algorithm that repeats the density ratio estimation and f-divergence minimization. Our algorithm offers a new perspective toward the understanding of GANs and is able to make use of multiple viewpoints obtained in the research of density ratio estimation, e.g. what divergence is stable and relative density ratio is useful.