Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation

Lars Nieradzik, Henrike Stephani, Janis Keuper

This paper introduces Top-GAP, a novel regularization technique that enhances the explainability and robustness of convolutional neural networks. By constraining the spatial size o…