TimeREISE: Time Series Randomized Evolving Input Sample Explanation

  title={TimeREISE: Time Series Randomized Evolving Input Sample Explanation},
  author={Dominique Mercier and Andreas R. Dengel and Sheraz Ahmed},
  journal={Sensors (Basel, Switzerland)},
Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intelligence addresses this problem. However, most interpretability methods align to the imaging modality by design. The paper introduces TimeREISE, a model agnostic attribution method that shows success in the context of time series classification. The… 
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