Evaluating the Visualization of What a Deep Neural Network Has Learned

Samek, Wojciech, Binder, Alexander, Montavon, Gregoire, Lapuschkin, Sebastian, Muller, Klaus-Robert·2017·IEEE Transactions on Neural Networks and Learning Systems
DOI 10.1109/tnnls.2016.2599820

Cited by (1,000)

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  14. Towards robust machine learning methods for the analysis of brain data
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    Neil Jethani, Adriel Saporta, Rajesh Ranganath · 2023 · PubMed
  23. The Generalizability of Explanations
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  25. M-Rule: An Enhanced Deep Taylor Decomposition for Multi-model Interpretability
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  29. Desirable Properties Based Neural Network Explanations Evaluation Method for Fault Diagnosis
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  61. Artificial Intelligence and Machine Learning in Bioinformatics
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  62. Fed2: Feature-Aligned Federated Learning
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  63. Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification
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  65. From measurement to machine learning: Towards analysing cognition
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    Marco Godi, Maedeh Aghaei, Mariella Dimiccoli, Marco Cristani · 2017
  67. EPiC Series in Computing
    Goran Frehse, Matthias Althoff · 2017 · Institute of Science and Technology Austria
  68. Exploring the intersections between Information Visualization and Machine Learning
    Igor Bueno Corrêa · 2019
  69. An Overview of Computational Approaches for Interpretation Analysis
    Philipp Blandfort, Hees, Jörn, Desmond U. Patton · 2018 · arXiv (Cornell University)
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    K. Robert Müller · 2019 · International Winter Conference on Brain-Computer Interface (BCI)
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    Cai Minjun · 2019 · International Education Technology and Research Conference
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  73. Discrimination Assessment for Saliency Maps
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  74. Estimation of Model Capacity for Image Classification
    Trupti R. Chavan, Abhijeet Vijay Nandedkar · 2019 · Lecture notes in electrical engineering
  75. Robust Hyperspectral Image Pan-Sharpening via Channel-Constrained Spatial Spectral Network
    Na Li, Licheng Liu · 2019 · Lecture notes in computer science
  76. Bilateral Sensitivity Analysis for Understandable Neural Networks and its application to Reservoir Engineering
    Jian Wang, Huaqing Zhang, Kai Zhang, Nikhil Ranjan Pal · 2020
  77. A Multisensory Learning Architecture for Rotation-invariant Object Recognition
    Murat Kırtay, Guido Schillaci, Verena V. Hafner · 2020 · arXiv (Cornell University)
  78. Learning an Unsupervised and Interpretable Representation of Emotion from Speech
    Siwei Wang, Catherine Soladié, Renaud Séguier · 2020 · Lecture notes in computer science
  79. Feature Acquisition for Facial Expression Recognition Using Deep Convolutional Neural Network
    Fan Dai, LI Wei-hua · 2020 · International Conference on Computer Science and Application Engineering (CSAE)
  80. Training with Adversaries to Improve Faithfulness of Attention in Neural Machine Translation
    Pooya Moradi, Nishant Kambhatla, Anoop Sarkar · 2020 · Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing: Student Research Workshop (AACL-IJCNLP SRW)
  81. Enhancing Dimensionality Reduction Techniques for Deep Neural Network Visualization
    Gabriel Dias Cantareira · 2020
  82. Balancing Robustness and Sensitivity using Feature Contrastive Learning
    Seung‐Yeon Kim, Daniel Gläsner, Srikumar Ramalingam, Cho‐Jui Hsieh, Kishore Papineni, Sanjiv Kumar · 2021 · arXiv (Cornell University)
  83. Interactive Visualization of 3D CNN Relevance Maps to Aid Model Comprehensibility
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  84. Peer Review #1 of "To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods (v0.1)"
    2021 · PeerJ Computer Science
  85. Peer Review #2 of "To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods (v0.1)"
    2021 · PeerJ Computer Science
  86. Peer Review #1 of "To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods (v0.2)"
    2021 · PeerJ Computer Science
  87. Peer Review #2 of "To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods (v0.2)"
    2021 · PeerJ Computer Science
  88. A general approach for Explanations in terms of Middle Level Features.
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  89. Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation
    Lin Geng Foo, Ho, Rui En, Jiamei Sun, Alexander Binder · 2020 · arXiv (Cornell University)
  90. Nachvollziehbare Künstliche Intelligenz: Methoden, Chancen und Risiken
    Marina Höhne · 2021 · Datenschutz und Datensicherheit - DuD
  91. Subspace Classification of Attention Deficit Hyperactivity Disorder with Laplacian Regularization
    Yuan Wang, Yuan Gao, Junping Jiang, Min Lin, Yibin Tang · 2021 · Lecture notes in computer science
  92. Activation-Based Cause Analysis Method for Neural Networks
    Yong Gyun Kim, Hyung Il Koo · 2021 · IEEE Access
  93. Towards a Better Understanding of Gradient-Based Explanatory Methods in NLP
    Qingfeng Du · 2021 · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering
  94. Calibrating Class Activation Maps for Long-Tailed Visual Recognition
    Chi Zhang, Guosheng Lin, Lvlong Lai, Henghui Ding, Qingyao Wu · 2021 · arXiv (Cornell University)
  95. Towards Learning a Vocabulary of Visual Concepts and Operators using Deep Neural Networks
    Sunil Kumar Vengalil, Neelam Sinha · 2021 · arXiv (Cornell University)
  96. Detection Accuracy for Evaluating Compositional Explanations of Units
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  98. A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
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  99. Multi-input and multi-output modeling method based on T-S fuzzy neural network and its application
    Haixu Ding, Jian Tang, Junfei Qiao · 2021 · International Conference on Industrial Artificial Intelligence (IAI)
  100. Debugging Tests for Model Explanations
    Adebayo, Julius, Muelly, Michael, Liccardi, Ilaria, Kim, Been · 2020 · arXiv