  Domain adaptive semantic segmentation is recognized as a promising technique
to alleviate the domain shift between the labeled source domain and the
unlabeled target domain in many real-world applications, such as automatic
pilot. However, large amounts of source domain data often introduce significant
costs in storage and training, and sometimes the source data is inaccessible
due to privacy policies. To address these problems, we investigate domain
adaptive semantic segmentation without source data, which assumes that the
model is pre-trained on the source domain, and then adapting to the target
domain without accessing source data anymore. Since there is no supervision
from the source domain data, many self-training methods tend to fall into the
``winner-takes-all'' dilemma, where the {\it majority} classes totally dominate
the segmentation networks and the networks fail to classify the {\it minority}
classes. Consequently, we propose an effective framework for this challenging
problem with two components: positive learning and negative learning. In
positive learning, we select the class-balanced pseudo-labeled pixels with
intra-class threshold, while in negative learning, for each pixel, we
investigate which category the pixel does not belong to with the proposed
heuristic complementary label selection. Notably, our framework can be easily
implemented and incorporated with other methods to further enhance the
performance. Extensive experiments on two widely-used synthetic-to-real
benchmarks demonstrate our claims and the effectiveness of our framework, which
outperforms the baseline with a large margin. Code is available at
\url{https://github.com/fumyou13/LDBE}.
