2026
When Random Saliency Looks Trained: Architectural Center Bias in CNN Interpretability
ICML 2026poster
Saliency maps are widely used to interpret image classification models and build trust in their predictions; however, their reliability remains a central concern, as randomized networks can produce saliency maps that closely resemble those of trained models. We identify a previously underappreciated…