BAdd: Bias Mitigation through Bias Addition

Computer vision datasets often exhibit biases in the form of spurious correlations between certain attributes and target variables. While recent efforts aim to mitigate such biases and foster bias-neutral representations, they fail in complex real-world scenarios. In particular, existing methods excel in controlled experiments on benchmarks with single-attribute injected biases, but struggle with complex multi-attribute biases that naturally occur in established CV datasets. In this paper, we introduce BAdd, a simple yet effective method that allows for learning bias-neutral representations invariant to bias-inducing attributes.  This is achieved by injecting features encoding these attributes into the training process. BAdd is evaluated on seven benchmarks and exhibits competitive performance, surpassing state-of-the-art methods on both single- and multi-attribute bias settings. Notably, it achieves +27.5% and +5.5% absolute accuracy improvements on the challenging multi-attribute benchmarks, FB-Biased-MNIST and CelebA, respectively.