This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Last update: Dec 23, 2022

Overview

Integrated Gradients

This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found here.

Acknowledgement

ankurtaly's tensorflow version

Requirements

python-3.5.2
pytorch-0.4.1
opencv-python

TODO List

add more functions as the original code.
finetune the results, make them close to the original paper.

Instructions

Highly recommend to use GPU to accelerate the computation. If you use CPU, I will recommend to select some small networks, such as resnet18. You also need to put your images under examples/.

Lists of networks that support (of course, you can add any networks by yourself)

inception
resnet18
resnet152
vgg19

Run the code

python main.py --cuda --model-type='inception' --img='01.jpg'

Results

Results are slightly different from the original paper, it may have some bugs or need to do some adjustments. I will keep updating it, any contributions are welcome!

This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Related tags

Overview

Integrated Gradients

Acknowledgement

Requirements

TODO List

Instructions

Lists of networks that support (of course, you can add any networks by yourself)

Run the code

Results

Inception-v3

ResNet-18

ResNet-152

VGG-19

Owner

Tianhong Dai

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