# Adjusting for Bias with Procedural Data

arXiv: https://arxiv.org/abs/2204.01108 · HTML: https://mlsanity.com/paper/2204.01108/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 3 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"](https://mlsanity.com/paper/1908.07201/) (2019)
- [Learning to See by Looking at Noise](https://mlsanity.com/paper/2106.05963/) (2021)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [Creatures great and SMAL: Recovering the shape and motion of animals from video](https://mlsanity.com/paper/1811.05804/) (2018)
- [Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery](https://mlsanity.com/paper/1711.09082/) (2017)
- [Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses](https://mlsanity.com/paper/2001.09528/) (2020)

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

- [CVB: A Video Dataset of Cattle Visual Behaviors](https://mlsanity.com/paper/2305.16555/) (2023)
- [Learning to Generate Parameters of ConvNets for Unseen Image Data](https://mlsanity.com/paper/2310.11862/) (2023)
- [Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics](https://mlsanity.com/paper/2401.13270/) (2024)
- [Improving the Accessibility of Dating Websites for Individuals with Visual Impairments](https://mlsanity.com/paper/2410.03695/) (2024)
- [A Review on Coarse to Fine-Grained Animal Action Recognition](https://mlsanity.com/paper/2506.01214/) (2025)
- [When Generative Augmentation Hurts: A Benchmark Study of GAN and Diffusion Models for Bias Correction in AI Classification Systems](https://mlsanity.com/paper/2603.16134/) (2026)

## Abstract (arXiv)

3D softwares are now capable of producing highly realistic images that look nearly indistinguishable from the real images. This raises the question: can real datasets be enhanced with 3D rendered data? We investigate this question. In this paper we demonstrate the use of 3D rendered data, procedural, data for the adjustment of bias in image datasets. We perform error analysis of images of animals which shows that the misclassification of some animal breeds is largely a data issue. We then create procedural images of the poorly classified breeds and that model further trained on procedural data can better classify poorly performing breeds on real data. We believe that this approach can be used for the enhancement of visual data for any underrepresented group, including rare diseases, or any data bias potentially improving the accuracy and fairness of models. We find that the resulting representations rival or even out-perform those learned directly from real data, but that good performance requires care in the 3D rendered procedural data generation. 3D image dataset can be viewed as a compressed and organized copy of a real dataset, and we envision a future where more and more procedural data proliferate while datasets become increasingly unwieldy, missing, or private. This paper suggests several techniques for dealing with visual representation learning in such a future.