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Perturbation-based methods for explaining deep neural networks: A survey

  • Published:2021-10 Issue: Volume:150 Page:228-234
  • ISSN:0167-8655
  • Container-title:Pattern Recognition Letters
  • language:en
  • Short-container-title:Pattern Recognition Letters

Author:

Ivanovs Maksims,Kadikis Roberts,Ozols Kaspars

Funder

Electronic Components and Systems for European Leadership

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference74 articles.

1. Trends and trajectories for explainable, accountable and intelligible systems: an HCI research agenda;Abdul,2018

2. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI);Adadi;IEEE Access,2018

3. Sanity checks for saliency maps;Adebayo,2018

4. Excitation backprop for RNNs;Adel Bargal,2018

5. Interpretable machine learning in healthcare;Ahmad,2018

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