Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

Wagner, Jörg, Köhler, Jan Mathias, Gindele, Tobias, Hetzel, Leon, Wiedemer, Jakob Thaddäus, Behnke, Sven·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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  21. Interpretability of Neural Network Binary Classification with Part Analysis
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  22. Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification
    Martin Charachon, Céline Hudelot, Paul-Henry Cournède, Camille Ruppli, Roberto Ardon · 2020 · arXiv (Cornell University)
  23. Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
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    Andrew J. Elliott, Stephen Law, Chris Russell · 2019 · arXiv (Cornell University)
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  33. FIMF Score-CAM: Score-CAM Based Visual Explanations via Fast Integrating Multiple Features of Local Space for Deep Networks
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