# A Novel Framework for Selection of GANs for an Application

arXiv: https://arxiv.org/abs/2002.08641 · HTML: https://mlsanity.com/paper/2002.08641/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Scenario generation for market risk models using generative neural networks](https://mlsanity.com/paper/2109.10072/) (2021)
- [Diffusion-Based Approaches in Medical Image Generation and Analysis](https://mlsanity.com/paper/2412.16860/) (2024)
- [Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild](https://mlsanity.com/paper/2211.14017/) (2022)
- [BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs](https://mlsanity.com/paper/2510.26892/) (2025)

## Abstract (arXiv)

Generative Adversarial Network (GAN) is a current focal point of research. The body of knowledge is fragmented, leading to a trial-error method while selecting an appropriate GAN for a given scenario. We provide a comprehensive summary of the evolution of GANs starting from its inception addressing issues like mode collapse, vanishing gradient, unstable training and non-convergence. We also provide a comparison of various GANs from the application point of view, its behaviour and implementation details. We propose a novel framework to identify candidate GANs for a specific use case based on architecture, loss, regularization and divergence. We also discuss application of the framework using an example, and we demonstrate a significant reduction in search space. This efficient way to determine potential GANs lowers unit economics of AI development for organizations.