  Machine learning models that can generalize to unseen domains are essential
when applied in real-world scenarios involving strong domain shifts. We address
the challenging domain generalization (DG) problem, where a model trained on a
set of source domains is expected to generalize well in unseen domains without
any exposure to their data. The main challenge of DG is that the features
learned from the source domains are not necessarily present in the unseen
target domains, leading to performance deterioration. We assume that learning a
richer set of features is crucial to improve the transfer to a wider set of
unknown domains. For this reason, we propose COLUMBUS, a method that enforces
new feature discovery via a targeted corruption of the most relevant input and
multi-level representations of the data. We conduct an extensive empirical
evaluation to demonstrate the effectiveness of the proposed approach which
achieves new state-of-the-art results by outperforming 18 DG algorithms on
multiple DG benchmark datasets in the DomainBed framework.
