# Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)

arXiv: https://arxiv.org/abs/1906.04493 · HTML: https://mlsanity.com/paper/1906.04493/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 17 of 17)

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- [Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules](https://mlsanity.com/paper/2210.06184/) (2022)
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- [Dynamically Masked Discriminator for Generative Adversarial Networks](https://mlsanity.com/paper/2306.07716/) (2023)
- [Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable](https://mlsanity.com/paper/2405.00466/) (2024)
- [Improving GAN Training via Feature Space Shrinkage](https://mlsanity.com/paper/2303.01559/) (2023)
- [CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing](https://mlsanity.com/paper/2305.02865/) (2023)
- [The Built-In Robustness of Decentralized Federated Averaging to Bad Data](https://mlsanity.com/paper/2502.18097/) (2025)
- [Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records](https://mlsanity.com/paper/2606.08903/) (2026)

## Abstract (arXiv)

I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings: (i) Artificial Curiosity (AC, 1990) is based on two such networks. One network learns to generate a probability distribution over outputs, the other learns to predict effects of the outputs. Each network minimizes the objective function maximized by the other. (ii) Generative Adversarial Networks (GANs, 2010-2014) are an application of AC where the effect of an output is 1 if the output is in a given set, and 0 otherwise. (iii) Predictability Minimization (PM, 1990s) models data distributions through a neural encoder that maximizes the objective function minimized by a neural predictor of the code components. I correct a previously published claim that PM is not based on a minimax game.