# EvoGAN: An Evolutionary Computation Assisted GAN

arXiv: https://arxiv.org/abs/2110.11583 · HTML: https://mlsanity.com/paper/2110.11583/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 20 of 20)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing](https://mlsanity.com/paper/1904.09709/) (2019)
- [Invertible Conditional GANs for image editing](https://mlsanity.com/paper/1611.06355/) (2016)
- [Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses](https://mlsanity.com/paper/2003.05905/) (2020)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification](https://mlsanity.com/paper/2006.15353/) (2020)
- [ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks](https://mlsanity.com/paper/1907.12859/) (2019)
- [Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/1910.04966/) (2019)
- [Deep Identity-aware Transfer of Facial Attributes](https://mlsanity.com/paper/1610.05586/) (2016)
- [Exploring the Evolution of GANs through Quality Diversity](https://mlsanity.com/paper/2007.06251/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models](https://mlsanity.com/paper/2409.00055/) (2024)
- [Robust Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2502.16129/) (2025)
- [GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation](https://mlsanity.com/paper/2401.00314/) (2023)
- [Binary Hypothesis Testing for Softmax Models and Leverage Score Models](https://mlsanity.com/paper/2405.06003/) (2024)
- [Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features](https://mlsanity.com/paper/2406.19815/) (2024)

## Abstract (arXiv)

The image synthesis technique is relatively well established which can generate facial images that are indistinguishable even by human beings. However, all of these approaches uses gradients to condition the output, resulting in the outputting the same image with the same input. Also, they can only generate images with basic expression or mimic an expression instead of generating compound expression. In real life, however, human expressions are of great diversity and complexity. In this paper, we propose an evolutionary algorithm (EA) assisted GAN, named EvoGAN, to generate various compound expressions with any accurate target compound expression. EvoGAN uses an EA to search target results in the data distribution learned by GAN. Specifically, we use the Facial Action Coding System (FACS) as the encoding of an EA and use a pre-trained GAN to generate human facial images, and then use a pre-trained classifier to recognize the expression composition of the synthesized images as the fitness function to guide the search of the EA. Combined random searching algorithm, various images with the target expression can be easily sythesized. Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN.