A cycle-free target-guided GAN that transfers source images into target style, combined with self-ensembling, achieves state-of-the-art synthetic-to-real semantic segmentation adaptation on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.
No more discrimina- tion: Cross city adaptation of road scene segmenters
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Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
A cycle-free target-guided GAN that transfers source images into target style, combined with self-ensembling, achieves state-of-the-art synthetic-to-real semantic segmentation adaptation on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.