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Benchmarking the Robustness of Semantic Segmentation Models
When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions.
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers
With respect to our 16 different types of image corruptions and 5 different network backbones, we are in 74% better than training with clean data.
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