• Corpus ID: 212725475

Towards Ground Truth Evaluation of Visual Explanations

@article{Osman2020TowardsGT,
  title={Towards Ground Truth Evaluation of Visual Explanations},
  author={Ahmed Osman and Leila Arras and Wojciech Samek},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.07258}
}
Several methods have been proposed to explain the decisions of neural networks in the visual domain via saliency heatmaps (aka relevances/feature importance scores). Thus far, these methods were mainly validated on real-world images, using either pixel perturbation experiments or bounding box localization accuracies. In the present work, we propose instead to evaluate explanations in a restricted and controlled setup using a synthetic dataset of rendered 3D shapes. To this end, we generate a… 

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