Before revision: We consider the problem of domain generalization , in which a predictor is trained on data drawn from a family of related training domains and tested on a distinct and unseen test domain.
After revision: Despite remarkable success in a variety of applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data. Toward addressing this challenge, we consider the domain generalization problem, wherein predictors are trained using data drawn from a family of related training domains and tested on a distinct and unseen test domain.