Mar 13, 2023 · Here, we present a method for obtaining uncertainty scores from pixel-wise loss gradients which can be computed efficiently during inference.

This work presents a method for obtaining uncertainty scores from pixel-wise loss gradients which can be computed efficiently during inference and shows ...

Mar 6, 2024 · In this work we propose an end-to-end trainable supervised Deep Convolutional Neural Network (DCNN) targeting the task of semantic-segmentation ...

The present code was utilized to generate the results in "Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution ...

Jul 31, 2023 · Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation. Kira Maag, Tobias Riedlinger.

Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation. K Maag, T Riedlinger. Proceedings of the 19th ...

Mar 15, 2023 · Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation https://lnkd.in/ekm-2GuK. No ...

We also show that the aggregate pixel uncertainty across an image can be used as a metric for reliable detection of out-of-distribution data. 1. Introduction.

Here we present an approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process.

Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation. Kira Maag, Tobias Riedlinger. Jan 17 2024. cs.CV.