  We present a unified invariance framework for supervised neural networks that
can induce independence to nuisance factors of data without using any nuisance
annotations, but can additionally use labeled information about biasing factors
to force their removal from the latent embedding for making fair predictions.
Invariance to nuisance is achieved by learning a split representation of data
through competitive training between the prediction task and a reconstruction
task coupled with disentanglement, whereas that to biasing factors is brought
about by penalizing the network if the latent embedding contains any
information about them. We describe an adversarial instantiation of this
framework and provide analysis of its working. Our model outperforms previous
works at inducing invariance to nuisance factors without using any labeled
information about such variables, and achieves state-of-the-art performance at
learning independence to biasing factors in fairness settings.
