interpretation

Implementation of the paper https://arxiv.org/abs/2002.07985

Dependencies

• Python3
• Keras
• Tensorflow (1.x)
• Scipy
• Numpy
• Scikit-learn
• tqdm

Model

Use the pretrained model from keras-applicaiton https://keras.io/applications/#applications

Evaluation Metric

The evalution of N-Ord, S-Ord, TPN and TPS are included in evaluations.py.

Implementation of Attribution methods

A quick guide to the implementations for attribution methods

Saliency Map

Original Paper: https://arxiv.org/pdf/1312.6034.pdf

Implementation: KerasAttr.saliency_map() in \explainer\Attribution.py

Integrated Gradient

Original Paper: https://arxiv.org/pdf/1703.01365.pdf

Implementation: KerasAttr.integrated_grad() in \explainer\Attribution.py

Smooth Gradient

Original Paper: https://arxiv.org/pdf/1706.03825.pdf

Implementation: KerasAttr.smooth_grad() in \explainer\Attribution.py

DeepLIFT

Original Paper: https://arxiv.org/pdf/1704.02685.pdf

Implementation: Use the RevealCancner version by https://github.com/kundajelab/deeplift

Layerwise Relevance Propagation

Original Paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140

Paper discussing the implementation of LRP-$\alpha 2 \beta 1$: https://arxiv.org/pdf/1706.07979.pdf

Implementaion: KerasAttr.lrpa2b1() in \explainer\Attribution.py ,

​ which is a wrapper of https://github.com/atulshanbhag/Layerwise-Relevance-Propagation.

Guided Backbropagation

Original Paper: https://arxiv.org/pdf/1412.6806.pdf

Implementaion: GuidedModel() in \explainer\Attribution.py

GradCAM

Original Paper: https://arxiv.org/pdf/1610.02391.pdf

Implementaion: KerasAttr.gradcan() in \explainer\Attribution.py

​ which is a wrapper of the implementation by https://github.com/eclique/keras-gradcam