Novel Approach to Out-of-Distribution Segmentation Using Wasserstein Uncertainty

This research explores a novel method for identifying out-of-distribution data in image segmentation using Wasserstein-based evidential uncertainty. The approach likely addresses a critical challenge in deploying segmentation models in real-world scenarios where unexpected data is encountered.