Ablation Path Saliency
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Abstract (arXiv)
Various types of saliency methods have been proposed for explaining black-box classification. In image applications, this means highlighting the part of the image that is most relevant for the current decision. Unfortunately, the different methods may disagree and it can be hard to quantify how representative and faithful the explanation really is. We observe however that several of these methods can be seen as edge cases of a single, more general procedure based on finding a particular path through the classifier's domain. This offers additional geometric interpretation to the existing methods. We demonstrate furthermore that ablation paths can be directly used as a technique of its own right. This is able to compete with literature methods on existing benchmarks, while giving more fine-grained information and better opportunities for validation of the explanations' faithfulness.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 26 Sep 2022.
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References · 16
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2013Intriguing properties of neural networkscited by 5,114
- 2017Axiomatic Attribution for Deep Networkscited by 3,350
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
- 2018Sanity Checks for Saliency Mapscited by 1,149
- 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
- 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
- 2017The (Un)reliability of saliency methodscited by 331
4 more
- 2016Top-down Neural Attention by Excitation Backpropcited by 318
- 2019Understanding Deep Networks via Extremal Perturbations and Smooth Maskscited by 257
- 2017Real Time Image Saliency for Black Box Classifierscited by 246
- 2021Explanations for Occluded Imagescited by 14
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