Visualisierung und Verifikation

Use the gradient-weighted class activation mapping (Grad-CAM) technique to understand why a deep learning network makes its classification decisions. Grad-CAM, invented by Selvaraju and coauthors [1], uses the gradient of the classification score with respect to the convolutional features determined by the network in order to understand which parts of the image are most important for classification. This example uses the GoogLeNet pretrained network for images.

Use locally interpretable model-agnostic explanations (LIME) to understand why a deep neural network makes a classification decision.

Use the fast gradient sign method (FGSM) and the basic iterative method (BIM) to generate adversarial examples for a pretrained neural network.

Train a neural network that is robust to adversarial examples using a Jacobian regularization scheme.

Verify the adversarial robustness of a deep learning neural network.