  Deep Neural Networks inherit spurious correlations embedded in training data
and hence may fail to predict desired labels on unseen domains (or
environments), which have different distributions from the domain used in
training. Invariance Learning (IL) has been developed recently to overcome this
shortcoming; using training data in many domains, IL estimates such a predictor
that is invariant to a change of domain. However, the requirement of training
data in multiple domains is a strong restriction of IL, since it often needs
high annotation cost. We propose a novel IL framework to overcome this problem.
Assuming the availability of data from multiple domains for a higher level of
classification task, for which the labeling cost is low, we estimate an
invariant predictor for the target classification task with training data in a
single domain. Additionally, we propose two cross-validation methods for
selecting hyperparameters of invariance regularization to solve the issue of
hyperparameter selection, which has not been handled properly in existing IL
methods. The effectiveness of the proposed framework, including the
cross-validation, is demonstrated empirically, and the correctness of the
hyperparameter selection is proved under some conditions.
