# The Six Fronts of the Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1910.13076 · HTML: https://mlsanity.com/paper/1910.13076/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network](https://mlsanity.com/paper/2108.03857/) (2021)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs](https://mlsanity.com/paper/2111.15097/) (2021)

## Abstract (arXiv)

Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf. In this paper, we intend to help those researchers, by splitting that incoming wave into six "fronts": Architectural Contributions, Conditional Techniques, Normalization and Constraint Contributions, Loss Functions, Image-to-image Translations, and Validation Metrics. The division in fronts organizes literature into approachable blocks, ultimately communicating to the reader how the area is evolving. Previous surveys in the area, which this works also tabulates, focus on a few of those fronts, leaving a gap that we propose to fill with a more integrated, comprehensive overview. Here, instead of an exhaustive survey, we opt for a straightforward review: our target is to be an entry point to this vast literature, and also to be able to update experienced researchers to the newest techniques.