  Unsupervised domain adaptation (DA) has gained substantial interest in
semantic segmentation. However, almost all prior arts assume concurrent access
to both labeled source and unlabeled target, making them unsuitable for
scenarios demanding source-free adaptation. In this work, we enable source-free
DA by partitioning the task into two: a) source-only domain generalization and
b) source-free target adaptation. Towards the former, we provide theoretical
insights to develop a multi-head framework trained with a virtually extended
multi-source dataset, aiming to balance generalization and specificity. Towards
the latter, we utilize the multi-head framework to extract reliable target
pseudo-labels for self-training. Additionally, we introduce a novel conditional
prior-enforcing auto-encoder that discourages spatial irregularities, thereby
enhancing the pseudo-label quality. Experiments on the standard
GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes benchmarks show our superiority
even against the non-source-free prior-arts. Further, we show our compatibility
with online adaptation enabling deployment in a sequentially changing
environment.
