  It is widely recognized that deep neural networks are sensitive to bias in
the data. This means that during training these models are likely to learn
spurious correlations between data and labels, resulting in limited
generalization abilities and low performance. In this context, model debiasing
approaches can be devised aiming at reducing the model's dependency on such
unwanted correlations, either leveraging the knowledge of bias information or
not. In this work, we focus on the latter and more realistic scenario, showing
the importance of accurately predicting the bias-conflicting and bias-aligned
samples to obtain compelling performance in bias mitigation. On this ground, we
propose to conceive the problem of model bias from an out-of-distribution
perspective, introducing a new bias identification method based on anomaly
detection. We claim that when data is mostly biased, bias-conflicting samples
can be regarded as outliers with respect to the bias-aligned distribution in
the feature space of a biased model, thus allowing for precisely detecting them
with an anomaly detection method. Coupling the proposed bias identification
approach with bias-conflicting data upsampling and augmentation in a two-step
strategy, we reach state-of-the-art performance on synthetic and real benchmark
datasets. Ultimately, our proposed approach shows that the data bias issue does
not necessarily require complex debiasing methods, given that an accurate bias
identification procedure is defined.
