  We consider unsupervised domain adaptation (UDA) for semantic segmentation in
which the model is trained on a labeled source dataset and adapted to an
unlabeled target dataset. Unfortunately, current self-training methods are
susceptible to misclassified pseudo-labels resulting from erroneous
predictions. Since certain classes are typically associated with less reliable
predictions in UDA, reducing the impact of such pseudo-labels without skewing
the training towards some classes is notoriously difficult. To this end, we
propose an extensive cut-and-paste strategy (ECAP) to leverage reliable
pseudo-labels through data augmentation. Specifically, ECAP maintains a memory
bank of pseudo-labeled target samples throughout training and cut-and-pastes
the most confident ones onto the current training batch. We implement ECAP on
top of the recent method MIC and boost its performance on two synthetic-to-real
domain adaptation benchmarks. Notably, MIC+ECAP reaches an unprecedented
performance of 69.1 mIoU on the Synthia->Cityscapes benchmark. Our code is
available at https://github.com/ErikBrorsson/ECAP.
