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3D softwares are now capable of producing highly realistic images that looknearly indistinguishable from the real images. This raises the question: canreal datasets be enhanced with 3D rendered data? We investigate this question.In this paper we demonstrate the use of 3D rendered data, procedural, data forthe adjustment of bias in image datasets. We perform error analysis of imagesof animals which shows that the misclassification of some animal breeds islargely a data issue. We then create procedural images of the poorly classifiedbreeds and that model further trained on procedural data can better classifypoorly performing breeds on real data. We believe that this approach can beused for the enhancement of visual data for any underrepresented group,including rare diseases, or any data bias potentially improving the accuracyand fairness of models. We find that the resulting representations rival oreven out-perform those learned directly from real data, but that goodperformance requires care in the 3D rendered procedural data generation. 3Dimage dataset can be viewed as a compressed and organized copy of a realdataset, and we envision a future where more and more procedural dataproliferate while datasets become increasingly unwieldy, missing, or private.This paper suggests several techniques for dealing with visual representationlearning in such a future.

Adjusting for Bias with Procedural Data