Unsupervised Domain Alignment for Mitigating low-level Dataset Biases

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 demonstrate the superiority of our method and its potential to mitigate 
the effects of dataset bias during the inference stage.