# Quality Aware Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1911.03149 · HTML: https://mlsanity.com/paper/1911.03149/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents](https://mlsanity.com/paper/2201.00308/) (2022)
- [Accelerating Diffusion Models via Early Stop of the Diffusion Process](https://mlsanity.com/paper/2205.12524/) (2022)
- [Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders](https://mlsanity.com/paper/2202.09671/) (2022)
- [SOS: Score-based Oversampling for Tabular Data](https://mlsanity.com/paper/2206.08555/) (2022)
- [An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry](https://mlsanity.com/paper/2401.05579/) (2024)
- [Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data](https://mlsanity.com/paper/2506.23174/) (2025)
- [Energy-Calibrated VAE with Test Time Free Lunch](https://mlsanity.com/paper/2311.04071/) (2023)
- [Self Sparse Generative Adversarial Networks](https://mlsanity.com/paper/2101.10556/) (2021)
- [Quality Map Fusion for Adversarial Learning](https://mlsanity.com/paper/2110.12338/) (2021)
- [Dendrogram distance: an evaluation metric for generative networks using hierarchical clustering](https://mlsanity.com/paper/2311.16894/) (2023)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) have become a very popular tool for implicitly learning high-dimensional probability distributions. Several improvements have been made to the original GAN formulation to address some of its shortcomings like mode collapse, convergence issues, entanglement, poor visual quality etc. While a significant effort has been directed towards improving the visual quality of images generated by GANs, it is rather surprising that objective image quality metrics have neither been employed as cost functions nor as regularizers in GAN objective functions. In this work, we show how a distance metric that is a variant of the Structural SIMilarity (SSIM) index (a popular full-reference image quality assessment algorithm), and a novel quality aware discriminator gradient penalty function that is inspired by the Natural Image Quality Evaluator (NIQE, a popular no-reference image quality assessment algorithm) can each be used as excellent regularizers for GAN objective functions. Specifically, we demonstrate state-of-the-art performance using the Wasserstein GAN gradient penalty (WGAN-GP) framework over CIFAR-10, STL10 and CelebA datasets.