  It is desirable to transfer the knowledge stored in a well-trained source
model onto non-annotated target domain in the absence of source data. However,
state-of-the-art methods for source free domain adaptation (SFDA) are subject
to strict limits: 1) access to internal specifications of source models is a
must; and 2) pseudo labels should be clean during self-training, making
critical tasks relying on semantic segmentation unreliable. Aiming at these
pitfalls, this study develops a domain adaptive solution to semantic
segmentation with pseudo label rectification (namely \textit{PR-SFDA}), which
operates in two phases: 1) \textit{Confidence-regularized unsupervised
learning}: Maximum squares loss applies to regularize the target model to
ensure the confidence in prediction; and 2) \textit{Noise-aware pseudo label
learning}: Negative learning enables tolerance to noisy pseudo labels in
training, meanwhile positive learning achieves fast convergence. Extensive
experiments have been performed on domain adaptive semantic segmentation
benchmark, \textit{GTA5 $\to$ Cityscapes}. Overall, \textit{PR-SFDA} achieves a
performance of 49.0 mIoU, which is very close to that of the state-of-the-art
counterparts. Note that the latter demand accesses to the source model's
internal specifications, whereas the \textit{PR-SFDA} solution needs none as a
sharp contrast.
