Data representations that contain all the information about target variables
but are invariant to nuisance factors benefit supervised learning algorithms by
preventing them from learning associations between these factors and the
targets, thus reducing overfitting. We present a novel unsupervised invariance
induction framework for neural networks that learns a split representation of
data through competitive training between the prediction task and a
reconstruction task coupled with disentanglement, without needing any labeled
information about nuisance factors or domain knowledge. We describe an
adversarial instantiation of this framework and provide analysis of its
working. Our unsupervised model outperforms state-of-the-art methods, which are
supervised, at inducing invariance to inherent nuisance factors, effectively
using synthetic data augmentation to learn invariance, and domain adaptation.
Our method can be applied to any prediction task, eg., binary/multi-class
classification or regression, without loss of generality.

该论文提出了一种新的无监督不变感知诱导框架，该框架通过竞争式训练和分解重构任务学习数据的分离表示，以抵抗干扰因素，并且无需任何标签的信息或领域知识。该无监督模型可以有效地使用合成数据增强来学习不变性和领域适应，并在诸如二元 / 多类别分类或回归等任何预测任务中应用。