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.
There are many consistent explana- tions of unlabeled data: Why you should average
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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.