# Barcode Method for Generative Model Evaluation driven by Topological Data Analysis

arXiv: https://arxiv.org/abs/2106.02207 · HTML: https://mlsanity.com/paper/2106.02207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)

## Abstract (arXiv)

Evaluating the performance of generative models in image synthesis is a challenging task. Although the Fr'echet Inception Distance is a widely accepted evaluation metric, it integrates different aspects (e.g., fidelity and diversity) of synthesized images into a single score and assumes the normality of embedded vectors. Recent methods such as precision-and-recall and its variants such as density-and-coverage have been developed to separate fidelity and diversity based on k-nearest neighborhood methods. In this study, we propose an algorithm named barcode, which is inspired by the topological data analysis and is almost free of assumption and hyperparameter selections. In extensive experiments on real-world datasets as well as theoretical approach on high-dimensional normal samples, it was found that the 'usual' normality assumption of embedded vectors has several drawbacks. The experimental results demonstrate that barcode outperforms other methods in evaluating fidelity and diversity of GAN outputs. Official codes can be found in https://github.com/minjeekim00/Barcode.