Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes

Published: 21 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: generative models, generative modeling, bias, GANs, debiasing
TL;DR: We develop a method to debias pretrained unconditional generative models, such that they produce an even number of sample for each group.
Abstract: Generative models are being increasingly used in science and industry applications. Unfortunately, they often perpetuate the biases present in their training sets, such as societal biases causing certain groups to be underrepresented in the data. For instance, image generators may overwhelmingly produce images of white people due to few non-white samples in their training data. It is imperative to debias generative models so they synthesize an equal number of instances for each group, while not requiring retraining of the model to avoid prohibitive expense. We thus propose a *distribution mapping module* that produces samples from a *fair noise distribution*, such that the pretrained generative model produces *semantically uniform* outputs - an equal number of instances for each group - when conditioned on these samples. This does *not* involve retraining the generator, nor does it require *any* real training data. Experiments on debiasing generators trained on popular real-world datasets show that our method outperforms existing approaches.
Supplementary Material: zip
Submission Number: 8163