IS-CAM: Integrated Score-CAM for axiomatic-based explanations

Convolutional Neural Networks have been known as black-box models as humans cannot interpret their inner functionalities. With an attempt to make CNNs more interpretable and trustworthy, we propose IS-CAM (Integrated Score-CAM), where we introduce the integration operation within the Score-CAM pipeline to achieve visually sharper attribution maps quantitatively. Our method is evaluated on 2000 randomly selected images from the ILSVRC 2012 Validation dataset, which proves the versatility of IS-CAM to account for different models and methods.

[1]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[2]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Rakshit Naidu,et al.  SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization , 2020, ArXiv.

[4]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[5]  Ankur Taly,et al.  Axiomatic Attribution for Deep Networks , 2017, ICML.

[6]  Zijian Zhang,et al.  Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Daniel Omeiza,et al.  Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models , 2019, ArXiv.

[9]  Vineeth N. Balasubramanian,et al.  Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[10]  Kate Saenko,et al.  RISE: Randomized Input Sampling for Explanation of Black-box Models , 2018, BMVC.

[11]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.