Corpus ID: 203610196

Leveraging Model Interpretability and Stability to increase Model Robustness

@article{Wu2019LeveragingMI,
  title={Leveraging Model Interpretability and Stability to increase Model Robustness},
  author={Fei Wu and Thomas Michel and Alexandre Briot},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.00387}
}
State of the art Deep Neural Networks (DNN) can now achieve above human level accuracy on image classification tasks. However their outstanding performances come along with a complex inference mechanism making them arduously interpretable models. In order to understand the underlying prediction rules of DNNs, Dhamdhere et al. propose an interpretability method to break down a DNN prediction score as sum of its hidden unit contributions, in the form of a metric called conductance. Analyzing… Expand

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