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ACE: Adapting to Changing Environments for Semantic Segmentation
[article]
2019
arXiv
pre-print
Deep neural networks exhibit exceptional accuracy when they are trained and tested on the same data distributions. However, neural classifiers are often extremely brittle when confronted with domain shift---changes in the input distribution that occur over time. We present ACE, a framework for semantic segmentation that dynamically adapts to changing environments over the time. By aligning the distribution of labeled training data from the original source domain with the distribution of
arXiv:1904.06268v1
fatcat:or4ha7zi4zgdto3fin4orapzla