  Generalizing visual recognition models trained on a single distribution to
unseen input distributions (i.e. domains) requires making them robust to
superfluous correlations in the training set. In this work, we achieve this
goal by altering the training images to simulate new domains and imposing
consistent visual attention across the different views of the same sample. We
discover that the first objective can be simply and effectively met through
visual corruptions. Specifically, we alter the content of the training images
using the nineteen corruptions of the ImageNet-C benchmark and three additional
transformations based on Fourier transform. Since these corruptions preserve
object locations, we propose an attention consistency loss to ensure that class
activation maps across original and corrupted versions of the same training
sample are aligned. We name our model Attention Consistency on Visual
Corruptions (ACVC). We show that ACVC consistently achieves the state of the
art on three single-source domain generalization benchmarks, PACS, COCO, and
the large-scale DomainNet.
