Learning Debiased Representation via Disentangled Feature Augmentation

Image classification models tend to make decisions based on peripheral attributes of data items that have strong correlation with a target variable ( i.e., dataset bias). These biased models suffer from the poor generalization capability when evaluated on unbiased datasets.