Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation

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), an 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, leading to state-of-the-art performance within a simple to implement and fast to converge approach.