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Dataset bias is a well-known problem in the field of computer vision. Thepresence of implicit bias in any image collection hinders a model trained andvalidated on a particular dataset to yield similar accuracies when tested onother datasets. In this paper, we propose a novel debiasing technique to reducethe effects of a biased training dataset. Our goal is to augment the trainingdata using a generative network by learning a non-linear mapping from thesource domain (training set) to the target domain (testing set) while retainingtraining set labels. The cycle consistency loss and adversarial loss forgenerative adversarial networks are used to learn the mapping. A structuredsimilarity index (SSIM) loss is used to enforce label retention whileaugmenting the training set. Our methods and hypotheses are supported byquantitative comparisons with prior debiasing techniques. These comparisonsshowcase the superiority of our method and its potential to mitigate theeffects of dataset bias during the inference stage.

Unsupervised Domain Alignment to Mitigate Low Level Dataset Biases