  In recent years, semantic segmentation has taken benefit from various works
in computer vision. Inspired by the very versatile CycleGAN architecture, we
combine semantic segmentation with the concept of cycle consistency to enable a
multitask training protocol. However, learning is largely prevented by the
so-called steganography effect, which expresses itself as watermarks in the
latent segmentation domain, making image reconstruction a too easy task. To
combat this, we propose a noise injection, based either on quantization noise
or on Gaussian noise addition to avoid this disadvantageous information flow in
the cycle architecture. We find that noise injection significantly reduces the
generation of watermarks and thus allows the recognition of highly relevant
classes such as "traffic signs", which are hardly detected by the ERFNet
baseline. We report mIoU and PSNR results on the Cityscapes dataset for
semantic segmentation and image reconstruction, respectively. The proposed
methodology allows to achieve an mIoU improvement on the Cityscapes validation
set of 5.7% absolute over the same CycleGAN without noise injection, and still
an absolute 4.9% over the ERFNet non-cyclic baseline.
