MetaCAM: Ensemble-Based Class Activation Map
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. Class Activation Maps (CAMs) are an increasingly
popular category of visual explanation methods for Convolutional Neural
Networks (CNNs). However, the performance of individual CAMs depends largely on
experimental parameters such as the selected image, target class, and model.
Here, we propose MetaCAM, an ensemble-based method for combining multiple
existing CAM methods based on the consensus of the top-k% most highly activated
pixels across component CAMs. We perform experiments to quantifiably determine
the optimal combination of 11 CAMs for a given MetaCAM experiment. A new method
denoted Cumulative Residual Effect (CRE) is proposed to summarize large-scale
ensemble-based experiments. We also present adaptive thresholding and
demonstrate how it can be applied to individual CAMs to improve their
performance, measured using pixel perturbation method Remove and Debias (ROAD).
Lastly, we show that MetaCAM outperforms existing CAMs and refines the most
salient regions of images used for model predictions. In a specific example,
MetaCAM improved ROAD performance to 0.393 compared to 11 individual CAMs with
ranges from -0.101-0.172, demonstrating the importance of combining CAMs
through an ensembling method and adaptive thresholding.