  A thriving trend for domain adaptive segmentation endeavors to generate the
high-quality pseudo labels for target domain and retrain the segmentor on them.
Under this self-training paradigm, some competitive methods have sought to the
latent-space information, which establishes the feature centroids (a.k.a
prototypes) of the semantic classes and determines the pseudo label candidates
by their distances from these centroids. In this paper, we argue that the
latent space contains more information to be exploited thus taking one step
further to capitalize on it. Firstly, instead of merely using the source-domain
prototypes to determine the target pseudo labels as most of the traditional
methods do, we bidirectionally produce the target-domain prototypes to degrade
those source features which might be too hard or disturbed for the adaptation.
Secondly, existing attempts simply model each category as a single and
isotropic prototype while ignoring the variance of the feature distribution,
which could lead to the confusion of similar categories. To cope with this
issue, we propose to represent each category with multiple and anisotropic
prototypes via Gaussian Mixture Model, in order to fit the de facto
distribution of source domain and estimate the likelihood of target samples
based on the probability density. We apply our method on GTA5->Cityscapes and
Synthia->Cityscapes tasks and achieve 61.2 and 62.8 respectively in terms of
mean IoU, substantially outperforming other competitive self-training methods.
Noticeably, in some categories which severely suffer from the categorical
confusion such as "truck" and "bus", our method achieves 56.4 and 68.8
respectively, which further demonstrates the effectiveness of our design.
