  Semantic segmentation aims to predict pixel-level labels. It has become a
popular task in various computer vision applications. While fully supervised
segmentation methods have achieved high accuracy on large-scale vision
datasets, they are unable to generalize on a new test environment or a new
domain well. In this work, we first introduce a new Un-aligned Domain Score to
measure the efficiency of a learned model on a new target domain in
unsupervised manner. Then, we present the new Bijective Maximum
Likelihood(BiMaL) loss that is a generalized form of the Adversarial Entropy
Minimization without any assumption about pixel independence. We have evaluated
the proposed BiMaL on two domains. The proposed BiMaL approach consistently
outperforms the SOTA methods on empirical experiments on "SYNTHIA to
Cityscapes", "GTA5 to Cityscapes", and "SYNTHIA to Vistas".
