The proposed norm-regularized adversarial training framework can be extended to other types of interpretation methods by adapting the regularization penalties to suit the specific characteristics of each method. For integrated gradients, which consider the integral of gradients along the path from a baseline to the input, the regularization penalties can be designed to promote smoothness and consistency along the integrated path. This can be achieved by incorporating penalties that encourage gradual changes in the attribution scores along the path, ensuring that the saliency maps are coherent and interpretable.
Similarly, for GradCAM, which highlights important regions in an image by leveraging the gradients of the target class score with respect to the feature maps, the regularization penalties can focus on enhancing the spatial consistency and relevance of the highlighted regions. By penalizing noisy or irrelevant activations in the feature maps, the adversarial training can guide the model to produce more accurate and meaningful heatmaps with GradCAM.
By customizing the regularization penalties to align with the specific requirements of each interpretation method, the norm-regularized adversarial training framework can effectively enhance the interpretability and robustness of a wide range of interpretation techniques beyond simple gradients.