  Dataset bias is a well-known problem in the field of computer vision. The
presence of implicit bias in any image collection hinders a model trained and
validated on a particular dataset to yield similar accuracies when tested on
other datasets. In this paper, we propose a novel debiasing technique to reduce
the effects of a biased training dataset. Our goal is to augment the training
data using a generative network by learning a non-linear mapping from the
source domain (training set) to the target domain (testing set) while retaining
training set labels. The cycle consistency loss and adversarial loss for
generative adversarial networks are used to learn the mapping. A structured
similarity index (SSIM) loss is used to enforce label retention while
augmenting the training set. Our methods and hypotheses are supported by
quantitative comparisons with prior debiasing techniques. These comparisons
showcase the superiority of our method and its potential to mitigate the
effects of dataset bias during the inference stage.
