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!
### Inception-v3
![inception](figures/inception.png)
### ResNet-18
![resnet18](figures/resnet18.png)
### ResNet-152
![resnet152](figures/resnet152.png)
### VGG-19
![vgg19](figures/vgg19.png)

Integrated Gradients

Acknowledgement

Requirements

TODO List

Instructions

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

Run the code