Gradient Based Interpretation

Gradient-based interpretation aims to understand the decision-making process of deep neural networks by analyzing gradients of the model's output with respect to its input. Current research focuses on improving the robustness and interpretability of these gradients, addressing issues like noise, sparsity, and the limitations of linear assumptions through techniques such as adversarial training, regularization (e.g., L1-norm), and novel algorithms like MoreauGrad. These advancements enhance the reliability and utility of gradient-based explanations, contributing to a better understanding of model behavior and facilitating more trustworthy applications of deep learning in various fields.