MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope

This is the official implementation of paper "MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope".

Applications

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Sparse Interpretation

Given an image and a neural network, our approach could let neural network explain how it make the decision.

Moreover, the interpretations are sparse (middle one) or group-sparse (right-most), revealing the most important pixels.

Robust Interpretation

Empircal experiments show that MoreauGrad is robust to adversarial interpretation attacks.

Dependencies

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The following is the least requirement to run MoreauGrad

• PyTorch, Torchvision with GPU support
• tqdm (Progess bar)

Usage

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The simpliest way to use MoreauGrad is:

import explainer
from data_util import plot_saliency

def sparse_interpret(net, image, label):
    # Initialize explainer
    sparse_explainer = explainer.SparseMoreauExplainer(MAX_ITR=200)
    # Get sparse explaination
    interpretation = sparse_explainer.explain(net, image, label)
    # Visualization
    plot_saliency(interpretation.detach(), 'path/to/save/interpretation.png')

The function arguments are:

• net: PyTorch network
• image: an image tensor with shape in [1, 3, n, n]
• label: the label for the image

We recommend the reader to try demo.py to generate an interpretation of the church image first. Then, the reader may modify interpret.py, which is the script we provide for custom network and dataset interpretation.