# How good is my GAN?

arXiv: https://arxiv.org/abs/1807.09499 · HTML: https://mlsanity.com/paper/1807.09499/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2018.

## Built on by (most cited first, 30 of 74)

- [Pros and Cons of GAN Evaluation Measures: New Developments](https://mlsanity.com/paper/2103.09396/) (2021)
- [Understanding Hallucinations in Diffusion Models through Mode Interpolation](https://mlsanity.com/paper/2406.09358/) (2024)
- [PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting](https://mlsanity.com/paper/2210.10542/) (2022)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?](https://mlsanity.com/paper/2402.01832/) (2024)
- [Adversarial random forests for density estimation and generative modeling](https://mlsanity.com/paper/2205.09435/) (2022)
- [Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images](https://mlsanity.com/paper/2308.02062/) (2023)
- [Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder](https://mlsanity.com/paper/2105.00026/) (2021)
- [Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case](https://mlsanity.com/paper/2206.08309/) (2022)
- [Toward Understanding Generative Data Augmentation](https://mlsanity.com/paper/2305.17476/) (2023)
- [A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars](https://mlsanity.com/paper/2401.04730/) (2024)
- [Inverting Generative Adversarial Renderer for Face Reconstruction](https://mlsanity.com/paper/2105.02431/) (2021)
- [LARGE: Latent-Based Regression through GAN Semantics](https://mlsanity.com/paper/2107.11186/) (2021)
- [Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach](https://mlsanity.com/paper/2405.02700/) (2024)
- [Generative Zero-shot Network Quantization](https://mlsanity.com/paper/2101.08430/) (2021)
- [Semantic Palette: Guiding Scene Generation with Class Proportions](https://mlsanity.com/paper/2106.01629/) (2021)
- [Generative Modeling for Multi-task Visual Learning](https://mlsanity.com/paper/2106.13409/) (2021)
- [A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training](https://mlsanity.com/paper/2203.13455/) (2022)
- [MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar](https://mlsanity.com/paper/2111.08195/) (2021)
- [On the Robustness of Quality Measures for GANs](https://mlsanity.com/paper/2201.13019/) (2022)
- [Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques](https://mlsanity.com/paper/2305.10118/) (2023)
- [PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification](https://mlsanity.com/paper/2104.01271/) (2021)
- [Exploring the Quality of GAN Generated Images for Person Re-Identification](https://mlsanity.com/paper/2108.09977/) (2021)
- [Beta-VAE Reproducibility: Challenges and Extensions](https://mlsanity.com/paper/2112.14278/) (2021)
- [RareGAN: Generating Samples for Rare Classes](https://mlsanity.com/paper/2203.10674/) (2022)
- [AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results](https://mlsanity.com/paper/2508.13479/) (2025)
- [TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets](https://mlsanity.com/paper/2105.06219/) (2021)
- [Improved Input Reprogramming for GAN Conditioning](https://mlsanity.com/paper/2201.02692/) (2022)
- [FedSyn: Synthetic Data Generation using Federated Learning](https://mlsanity.com/paper/2203.05931/) (2022)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)

## Abstract (arXiv)

Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification---GAN-train and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two measures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.