SourceFaithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
The paper computes the trace neural tangent kernel (trntk) for three pre-trained CIFAR10 classifiers and trains a kernel general linear model (KGLM) on the same training data. Faithfulness is measured by Kendall-τ rank correlation between the KGLM's and the NN's softmax probabilities for the correct class, plus test accuracy differential. All three models show high correlation (0.700–0.786) and near-zero accuracy differentials (−0.28% to −0.4%), indicating the trntk KGLM faithfully reproduces the NN's decision function. Among six kernel functions compared for ResNet18, the trntk achieves the highest τk (0.776) versus trntk0 (0.658), proj-trntk (0.737), proj-pntk (0.407), embedding kernel (0.768), and conjugate kernel (0.630).