# On the Evaluation of Generative Adversarial Networks By Discriminative Models

arXiv: https://arxiv.org/abs/2010.03549 · HTML: https://mlsanity.com/paper/2010.03549/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis](https://mlsanity.com/paper/2307.15424/) (2023)
- [Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images](https://mlsanity.com/paper/2201.10324/) (2022)
- [Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis](https://mlsanity.com/paper/2307.10166/) (2023)
- [Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images](https://mlsanity.com/paper/2309.12245/) (2023)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) can accurately model complex multi-dimensional data and generate realistic samples. However, due to their implicit estimation of data distributions, their evaluation is a challenging task. The majority of research efforts associated with tackling this issue were validated by qualitative visual evaluation. Such approaches do not generalize well beyond the image domain. Since many of those evaluation metrics are proposed and bound to the vision domain, they are difficult to apply to other domains. Quantitative measures are necessary to better guide the training and comparison of different GANs models. In this work, we leverage Siamese neural networks to propose a domain-agnostic evaluation metric: (1) with a qualitative evaluation that is consistent with human evaluation, (2) that is robust relative to common GAN issues such as mode dropping and invention, and (3) does not require any pretrained classifier. The empirical results in this paper demonstrate the superiority of this method compared to the popular Inception Score and are competitive with the FID score.