  Deep neural networks are known for their inability to learn robust
representations when biases exist in the dataset. This results in a poor
generalization to unbiased datasets, as the predictions strongly rely on
peripheral and confounding factors, which are erroneously learned by the
network. Many existing works deal with this issue by either employing an
explicit supervision on the bias attributes, or assuming prior knowledge about
the bias. In this work we study this problem in a more difficult scenario, in
which no explicit annotation about the bias is available, and without any prior
knowledge about its nature. We propose a fully unsupervised debiasing
framework, consisting of three steps: first, we exploit the natural preference
for learning malignant biases, obtaining a bias-capturing model; then, we
perform a pseudo-labelling step to obtain bias labels; finally we employ
state-of-the-art supervised debiasing techniques to obtain an unbiased model.
We also propose a theoretical framework to assess the biasness of a model, and
provide a detailed analysis on how biases affect the training of neural
networks. We perform experiments on synthetic and real-world datasets, showing
that our method achieves state-of-the-art performance in a variety of settings,
sometimes even higher than fully supervised debiasing approaches.
