Randomized Input Sampling for Explanation of Black-box Models (RISE)

The re-implementation of RISE with pytorch. This repo also includes the code of CAM and Grad-CAM.

Requirements

• python 3.x
• pytorch >= 0.4
• pillow
• numpy
• opencv
• matplotlib

How to use

You can use RISE as well as CAM, GradCAM as a model wrapper described in rise.py and cam.py. Please see jupyter notebook files for the detail.

Results

image
CAM
Grad-CAM
RISE

References

• RISE: Randomized Input Sampling for Explanation of Black-box Models,
  Vitali Petsiuk, Abir Das, Kate Saenko, In BMVC 2018 [paper] [official code]
• Learning Deep Features for Discriminative Localization, Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, In CVPR2016 [paper]
• Grad-CAM: Visual explanations from deep networks via gradient-based localization, Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra, [arXiv]