Deep generative models require large amounts of training data. This often
poses a problem as the collection of datasets can be expensive and difficult,
in particular datasets that are representative of the appropriate underlying
distribution (e.g. demographic). This introduces biases in datasets which are
further propagated in the models. We present an approach to mitigate biases in
an existing generative adversarial network by rebalancing the model
distribution. We do so by generating balanced data from an existing unbalanced
deep generative model using latent space exploration and using this data to
train a balanced generative model. Further, we propose a bias mitigation loss
function that shows improvements in the fairness metric even when trained with
unbalanced datasets. We show results for the Stylegan2 models while training on
the FFHQ dataset for racial fairness and see that the proposed approach
improves on the fairness metric by almost 5 times, whilst maintaining image
quality. We further validate our approach by applying it to an imbalanced
Cifar-10 dataset. Lastly, we argue that the traditionally used image quality
metrics such as Frechet inception distance (FID) are unsuitable for bias
mitigation problems