  Convolutional neural networks typically perform poorly when the test (target
domain) and training (source domain) data have significantly different
distributions. While this problem can be mitigated by using the target domain
data to align the source and target domain feature representations, the target
domain data may be unavailable due to privacy concerns. Consequently, there is
a need for methods that generalize well despite restricted access to target
domain data during training. In this work, we propose an adversarial semantic
hallucination approach (ASH), which combines a class-conditioned hallucination
module and a semantic segmentation module. Since the segmentation performance
varies across different classes, we design a semantic-conditioned style
hallucination module to generate affine transformation parameters from semantic
information in the segmentation probability maps of the source domain image.
Unlike previous adaptation approaches, which treat all classes equally, ASH
considers the class-wise differences. The segmentation module and the
hallucination module compete adversarially, with the hallucination module
generating increasingly "difficult" stylized images to challenge the
segmentation module. In response, the segmentation module improves as it is
trained with generated samples at an appropriate class-wise difficulty level.
Our results on the Cityscapes and Mapillary benchmark datasets show that our
method is competitive with state of the art work. Code is made available at
https://github.com/gabriel-tjio/ASH.
