scholar.google.com › citations
We propose a new technique called Blur Integrated Gradients (Blur IG). This ... Third, it eliminates the need for baseline parameter for Integrated Gradients for ...
People also ask
What are integrated gradients?
What is integrated gradients for Bert?
What is the difference between integrated gradients and saliency maps?
How many steps are needed for integrated gradients?
... input and prediction label using a path that successively blurs the image. ... Generates a saliency mask by computing integrated gradients for a given input.
Apr 21, 2022 · The idea presented by Fong and Vedaldi [3] blurred version of the image is a domain-specific way to represent missing information and therefore ...
We theo- retically demonstrate that the true gradient is orthogonal to the noise direction, resulting in the gradient's multiplication along the noise direction ...
Jan 10, 2020 · By integrating over a path, integrated gradients avoids problems with local gradients being saturated. ... For the blur baseline and the blur ...
Mar 15, 2024 · Specifically, the path of BlurIG is defined by successively blurring the input image with a Gaussian blur filter. ... Vanilla gradients and ...
Blur Integrated Gradients (Blur-IG) specifically addresses the issues that arise with choosing a specific baseline. In short, Blur-IG removes the need to ...
Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis.
Dec 17, 2021 · Integrated Gradients make it possible to examine the inputs of a deep learning model on their importance for the output.
Blur Integrated Gradients (BlurIG) [46]. Xu et al. [46] propose BlurIG ... clude Guided Integrated Gradients (GIG) [22] and Blur In- tegrated Gradients ...