  Most modern approaches for domain adaptive semantic segmentation rely on
continued access to source data during adaptation, which may be infeasible due
to computational or privacy constraints. We focus on source-free domain
adaptation for semantic segmentation, wherein a source model must adapt itself
to a new target domain given only unlabeled target data. We propose
Augmentation Consistency-guided Self-training (AUGCO), a source-free adaptation
algorithm that uses the model's pixel-level predictive consistency across
diverse, automatically generated views of each target image along with model
confidence to identify reliable pixel predictions, and selectively self-trains
on those. AUGCO achieves state-of-the-art results for source-free adaptation on
3 standard benchmarks for semantic segmentation, all within a simple to
implement and fast to converge method.
