ERM++: An Improved Baseline for Domain Generalization

arXiv:2304.01973v3 Announce Type: replace
Abstract: Domain Generalization (DG) measures a classifier's ability to generalize to new distributions of data it was not trained on. Recent work has shown that a hyperparameter-tuned Empirical Risk Minimization (ERM) training procedure, that is simply minimizing the empirical risk on the source domains, can outperform most existing DG methods. ERM has achieved such strong results while only tuning hyper-parameters such as learning rate, weight decay, batch size, and dropout. However there are additional hyperparameters which …

arxiv cs.cv cs.lg domain erm type