# Compound Frechet Inception Distance for Quality Assessment of GAN Created Images

arXiv: https://arxiv.org/abs/2106.08575 · HTML: https://mlsanity.com/paper/2106.08575/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)

## Built on by (most cited first, 8 of 8)

- [HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes](https://mlsanity.com/paper/2409.19833/) (2024)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [Brain Tumor Synthetic Data Generation with Adaptive StyleGANs](https://mlsanity.com/paper/2212.01772/) (2022)
- [PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification](https://mlsanity.com/paper/2405.07332/) (2024)
- [Prompt2Fashion: An automatically generated fashion dataset](https://mlsanity.com/paper/2409.06442/) (2024)
- [KDC-Diff: A Latent-Aware Diffusion Model with Knowledge Retention for Memory-Efficient Image Generation](https://mlsanity.com/paper/2505.06995/) (2025)
- [What FID Hides: Detecting, Ranking, and Diagnosing Deviations in Generative Evaluation](https://mlsanity.com/paper/2608.24881/) (2026)
- [Weak Segmentation-Guided GAN for~Realistic Color Edition](https://mlsanity.com/paper/2610.16620/) (2026)

## Abstract (arXiv)

Generative adversarial networks or GANs are a type of generative modeling framework. GANs involve a pair of neural networks engaged in a competition in iteratively creating fake data, indistinguishable from the real data. One notable application of GANs is developing fake human faces, also known as "deep fakes," due to the deep learning algorithms at the core of the GAN framework. Measuring the quality of the generated images is inherently subjective but attempts to objectify quality using standardized metrics have been made. One example of objective metrics is the Frechet Inception Distance (FID), which measures the difference between distributions of feature vectors for two separate datasets of images. There are situations that images with low perceptual qualities are not assigned appropriate FID scores. We propose to improve the robustness of the evaluation process by integrating lower-level features to cover a wider array of visual defects. Our proposed method integrates three levels of feature abstractions to evaluate the quality of generated images. Experimental evaluations show better performance of the proposed method for distorted images.