Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

@article{Lpez2021BeyondTC,
  title={Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations},
  author={Pau Rodr{\'i}guez L{\'o}pez and Massimo Caccia and Alexandre Lacoste and Lee Zamparo and Issam H. Laradji and Laurent Charlin and David V{\'a}zquez},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={1036-1045}
}
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model’s input to change its prediction, providing details about the model’s decision-making. Current methods tend to generate trivial counterfactuals about a model’s decisions, as they often suggest to exaggerate or remove… 

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