English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Explanation methods facilitate the development of models that learnmeaningful concepts and avoid exploiting spurious correlations. We illustrate apreviously unrecognized limitation of the popular neural network explanationmethod Grad-CAM: as a side effect of the gradient averaging step, Grad-CAMsometimes highlights locations the model did not actually use. To solve thisproblem, we propose HiResCAM, a novel class-specific explanation method that isguaranteed to highlight only the locations the model used to make eachprediction. We prove that HiResCAM is a generalization of CAM and explore therelationships between HiResCAM and other gradient-based explanation methods.Experiments on PASCAL VOC 2012, including crowd-sourced evaluations, illustratethat while HiResCAM's explanations faithfully reflect the model, Grad-CAM oftenexpands the attention to create bigger and smoother visualizations. Overall,this work advances convolutional neural network explanation approaches and mayaid in the development of trustworthy models for sensitive applications.

Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks