Manhattan and Correlation distance metrics best align CAM saliency maps with human perception on ImageNet chihuahuas, ranking LayerCAM, Score-CAM, and IS-CAM highest when compared to crowdsourced choices via RBO.
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Randomly initialized networks trained solely via peer-to-peer self-distillation learn useful representations that outperform random baselines on downstream tasks.
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How Can One Choose the Best CAM-Based Explainability Method for a CNN Model?
Manhattan and Correlation distance metrics best align CAM saliency maps with human perception on ImageNet chihuahuas, ranking LayerCAM, Score-CAM, and IS-CAM highest when compared to crowdsourced choices via RBO.
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Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus
Randomly initialized networks trained solely via peer-to-peer self-distillation learn useful representations that outperform random baselines on downstream tasks.