  Domain adaptive semantic segmentation methods commonly utilize stage-wise
training, consisting of a warm-up and a self-training stage. However, this
popular approach still faces several challenges in each stage: for warm-up, the
widely adopted adversarial training often results in limited performance gain,
due to blind feature alignment; for self-training, finding proper categorical
thresholds is very tricky. To alleviate these issues, we first propose to
replace the adversarial training in the warm-up stage by a novel symmetric
knowledge distillation module that only accesses the source domain data and
makes the model domain generalizable. Surprisingly, this domain generalizable
warm-up model brings substantial performance improvement, which can be further
amplified via our proposed cross-domain mixture data augmentation technique.
Then, for the self-training stage, we propose a threshold-free dynamic
pseudo-label selection mechanism to ease the aforementioned threshold problem
and make the model better adapted to the target domain. Extensive experiments
demonstrate that our framework achieves remarkable and consistent improvements
compared to the prior arts on popular benchmarks. Codes and models are
available at https://github.com/fy-vision/DiGA
