Papers › Adjusting for Bias with Procedural Data
Adjusting for Bias with Procedural Data
Shesh Narayan Gupta, Nicholas Bear Brown
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Representation Learning | Animals-10 | top_model_weights_with_3d_2 | 1:1 Accuracy | 0.745896 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections