# IGAN: Inferent and Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2109.13360 · HTML: https://mlsanity.com/paper/2109.13360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 22 of 22)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Large Scale Adversarial Representation Learning](https://mlsanity.com/paper/1907.02544/) (2019)
- [Adversarially Learned Inference](https://mlsanity.com/paper/1606.00704/) (2016)
- [TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up](https://mlsanity.com/paper/2102.07074/) (2021)
- [Adversarial Latent Autoencoders](https://mlsanity.com/paper/2004.04467/) (2020)
- [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https://mlsanity.com/paper/1807.06358/) (2018)
- [ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching](https://mlsanity.com/paper/1709.01215/) (2017)
- [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings](https://mlsanity.com/paper/1711.05139/) (2017)
- [It Takes (Only) Two: Adversarial Generator-Encoder Networks](https://mlsanity.com/paper/1704.02304/) (2017)
- [Disentangled Inference for GANs with Latently Invertible Autoencoder](https://mlsanity.com/paper/1906.08090/) (2019)
- [Autoencoding Generative Adversarial Networks](https://mlsanity.com/paper/2004.05472/) (2020)
- [Decomposed Adversarial Learned Inference](https://mlsanity.com/paper/2004.10267/) (2020)

## Built on by (most cited first, 1 of 1)

- [Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction](https://mlsanity.com/paper/2504.03327/) (2025)

## Abstract (arXiv)

I present IGAN (Inferent Generative Adversarial Networks), a neural architecture that learns both a generative and an inference model on a complex high dimensional data distribution, i.e. a bidirectional mapping between data samples and a simpler low-dimensional latent space. It extends the traditional GAN framework with inference by rewriting the adversarial strategy in both the image and the latent space with an entangled game between data-latent encoded posteriors and priors. It brings a measurable stability and convergence to the classical GAN scheme, while keeping its generative quality and remaining simple and frugal in order to run on a lab PC. IGAN fosters the encoded latents to span the full prior space: this enables the exploitation of an enlarged and self-organised latent space in an unsupervised manner. An analysis of previously published articles sets the theoretical ground for the proposed algorithm. A qualitative demonstration of potential applications like self-supervision or multi-modal data translation is given on common image datasets including SAR and optical imagery.