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

Visualizing the features captured by Convolutional Neural Networks (CNNs) isone of the conventional approaches to interpret the predictions made by thesemodels in numerous image recognition applications. Grad-CAM is a popularsolution that provides such a visualization by combining the activation mapsobtained from the model. However, the average gradient-based terms deployed inthis method underestimates the contribution of the representations discoveredby the model to its predictions. Addressing this problem, we introduce asolution to tackle this issue by computing the path integral of thegradient-based terms in Grad-CAM. We conduct a thorough analysis to demonstratethe improvement achieved by our method in measuring the importance of theextracted representations for the CNN's predictions, which yields to ourmethod's administration in object localization and model interpretation.

Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring