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.

本文提出了一种统一的不变性框架，可用于训练神经网络，从而使其在不使用任何附带标注的同时将数据的无关因素与数据相互独立，但也可以使用关于有偏因素的标记信息，强制将其从潜在嵌入中去除以实现公平预测。该模型在不使用标记信息的情况下优于先前的工作，并在公平性设置中实现了学习自由因素的最新性能。