CAManim: Animating end-to-end network activation maps

no code implementations • 19 Dec 2023 • Emily Kaczmarek, Olivier X. Miguel, Alexa C. Bowie, Robin Ducharme, Alysha L. J. Dingwall-Harvey, Steven Hawken, Christine M. Armour, Mark C. Walker, Kevin Dick

We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

MetaCAM: Ensemble-Based Class Activation Map

no code implementations • 31 Jul 2023 • Emily Kaczmarek, Olivier X. Miguel, Alexa C. Bowie, Robin Ducharme, Alysha L. J. Dingwall-Harvey, Steven Hawken, Christine M. Armour, Mark C. Walker, Kevin Dick

The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification.

Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation

no code implementations • 21 Mar 2021 • André M. Carrington, Douglas G. Manuel, Paul W. Fieguth, Tim Ramsay, Venet Osmani, Bernhard Wernly, Carol Bennett, Steven Hawken, Matthew McInnes, Olivia Magwood, Yusuf Sheikh, Andreas Holzinger

We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python.

Autonomous Driving Decision Making +2