Learnable edge detectors can make deep convolutional neural networks more robust.

ABSTRACT: Deep convolutional neural networks (DCNNs) are vulnerable to examples with small perturbations. Improving DCNNs' robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Inspired by the principal way that human eyes recognize objects, i.e., largely relying on the shape features, this paper first designs four learnable edge detectors as layer kernels and proposes a binary edge feature branch (BEFB) to learn the binary edge features, which can be easily integrated into any popular backbone. The four edge detectors can learn the horizontal, vertical, positive diagonal, and negative diagonal edge features, respectively, and the branch is stacked by multiple Sobel layers (using learnable edge detectors as kernels) and one thres

SUBMITTER: Ding J 

PROVIDER: S-EPMC12425196 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature