haofanwang / Score Cam

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

To appear at IEEE CVPR 2020 Workshop on Fair, Data Efficient and Trusted Computer Vision.

In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Score-CAM is a gradient-free visualization method, extended from Grad-CAM and Grad-CAM++. It achieves better visual performance and fairness for interpreting the decision making process.

Paper: Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks (Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel and Xia Hu.)

Demo Colab

Update

2020.8.18: Score-CAM has been merged into PaddlePaddle/InterpretDL.

2020.7.11: Score-CAM has been merged into keisen/tf-keras-vis.

2020.5.11: Score-CAM has been merged into utkuozbulak/pytorch-cnn-visualizations.

2020.3.24: Score-CAM has been merged into frgfm/torch-cam.

Citation

If you find this work or code is helpful in your research, please cite and star:

@inproceedings{wang2020score,
  title={Score-CAM: Score-weighted visual explanations for convolutional neural networks},
  author={Wang, Haofan and Wang, Zifan and Du, Mengnan and Yang, Fan and Zhang, Zijian and Ding, Sirui and Mardziel, Piotr and Hu, Xia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={24--25},
  year={2020}
}

Thanks

Utils are built on flashtorch, thanks for releasing this great work!

Contact

If you have any questions, feel free to contact me via: [email protected]