Attribute aware filter-drop for bias-invariant classification

The widespread applicability of deep learning based algorithms demands dedicated attention towards ensuring unbiased behavior. Biased feature learning (for or against a particular sub-group) might often result in unfair predictions. In order to address the above issue, this research proposes a novel Filter-Drop algorithm for learning unbiased representations. The proposed technique focuses on learning the features useful for predicting the biasing attribute (or the sensitive attribute), followed by their elimination while performing the primary classification task. To this effect, a multi-task network is trained, which prevents the features capturing the attribute variations from being used for the primary classification task. The efficacy of the proposed Filter-Drop technique is demonstrated on two facial analysis datasets: UTKFace dataset and FairFace dataset. The proposed technique achieves similar performance across different ethnicity groups while training with highly skewed training data as well.