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ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation
[article]
2020
arXiv
pre-print
While fully-supervised deep learning yields good models for urban scene semantic segmentation, these models struggle to generalize to new environments with different lighting or weather conditions for instance. In addition, producing the extensive pixel-level annotations that the task requires comes at a great cost. Unsupervised domain adaptation (UDA) is one approach that tries to address these issues in order to make such systems more scalable. In particular, self-supervised learning (SSL)
arXiv:2006.08658v1
fatcat:yakgyozlhneixagkpfs372rovu