MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope
This addresses the need for more reliable and customizable interpretation tools in deep learning, particularly for computer vision, but it is incremental as it builds on existing gradient-based methods.
The authors tackled the problem of gradient-based interpretation methods lacking robustness and flexibility for sparsity, proposing MoreauGrad to provide smooth, robust, and sparse explanations for neural network predictions, with empirical success on computer vision datasets.
Explaining the predictions of deep neural nets has been a topic of great interest in the computer vision literature. While several gradient-based interpretation schemes have been proposed to reveal the influential variables in a neural net's prediction, standard gradient-based interpretation frameworks have been commonly observed to lack robustness to input perturbations and flexibility for incorporating prior knowledge of sparsity and group-sparsity structures. In this work, we propose MoreauGrad as an interpretation scheme based on the classifier neural net's Moreau envelope. We demonstrate that MoreauGrad results in a smooth and robust interpretation of a multi-layer neural network and can be efficiently computed through first-order optimization methods. Furthermore, we show that MoreauGrad can be naturally combined with $L_1$-norm regularization techniques to output a sparse or group-sparse explanation which are prior conditions applicable to a wide range of deep learning applications. We empirically evaluate the proposed MoreauGrad scheme on standard computer vision datasets, showing the qualitative and quantitative success of the MoreauGrad approach in comparison to standard gradient-based interpretation methods.