  Artificial neural networks perform state-of-the-art in an ever-growing number
of tasks, and nowadays they are used to solve an incredibly large variety of
tasks. There are problems, like the presence of biases in the training data,
which question the generalization capability of these models. In this work we
propose EnD, a regularization strategy whose aim is to prevent deep models from
learning unwanted biases. In particular, we insert an "information bottleneck"
at a certain point of the deep neural network, where we disentangle the
information about the bias, still letting the useful information for the
training task forward-propagating in the rest of the model. One big advantage
of EnD is that we do not require additional training complexity (like decoders
or extra layers in the model), since it is a regularizer directly applied on
the trained model. Our experiments show that EnD effectively improves the
generalization on unbiased test sets, and it can be effectively applied on
real-case scenarios, like removing hidden biases in the COVID-19 detection from
radiographic images.
