mlsanityData to 7 Oct 2026

iNNvestigate neural networks!

arXiv 1808.04260 ↗First posted 13 Aug 2018cs.LG stat.MLcited by 58

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Abstract (arXiv)

In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand and analyze actions and pre- dictions, even of the most complex neural network architectures. Despite these arguments neural networks are often treated as black boxes. In the attempt to alleviate this short- coming many analysis methods were proposed, yet the lack of reference implementations often makes a systematic comparison between the methods a major effort. The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an analysis of image classifications for variety of state-of-the-art neural network architectures.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2018.

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Citing papers per year: 2021: 24; 2022: 15; 2023: 9; 2024: 6; 2025: 3; 2026: 1.

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  1. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  2. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  3. 2021Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAycited by 53
  4. 2024shapiq: Shapley Interactions for Machine Learningcited by 42
  5. 2022Explainable Deep Learning Methods in Medical Image Classification: A Surveycited by 31
  6. 2021Toward Explainable AI for Regression Modelscited by 30
  7. 2022Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspacescited by 25
  8. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 25
  9. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  10. 2024Position: Explain to Question not to Justifycited by 20
  11. 2021Scrutinizing XAI using linear ground-truth data with suppressor variablescited by 18
  12. 2021Fooling Partial Dependence via Data Poisoningcited by 16
38 more
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  2. 2021Model Explainability in Deep Learning Based Natural Language Processingcited by 14
  3. 2022Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteriacited by 11
  4. 2023Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approachescited by 9
  5. 2022Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clusteringcited by 8
  6. 2021Uncertainty aware and explainable diagnosis of retinal diseasecited by 7
  7. 2022Explain to Not Forget: Defending Against Catastrophic Forgetting with XAIcited by 7
  8. 2023XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importancecited by 7
  9. 2021COVID-19 detection using chest X-rays: is lung segmentation important for generalization?cited by 6
  10. 2021Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autismcited by 6
  11. 2022Gradient Backpropagation based Feature Attribution to Enable Explainable-AI on the Edgecited by 6
  12. 2021Predicting post-operative right ventricular failure using video-based deep learningcited by 5
  13. 2022Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagationcited by 5
  14. 2021Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphonescited by 3
  15. 2021LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Informationcited by 3
  16. 2021Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attributioncited by 3
  17. 2021Challenges for cognitive decoding using deep learning methodscited by 3
  18. 2021Evaluating deep transfer learning for whole-brain cognitive decodingcited by 3
  19. 2021Evaluating saliency methods on artificial data with different background typescited by 3
  20. 2022TSInterpret: A unified framework for time series interpretabilitycited by 3
  21. 2023False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA Classifierscited by 3
  22. 2021Towards Privacy-preserving Explanations in Medical Image Analysiscited by 2
  23. 2021Interpreting deep urban sound classification using Layer-wise Relevance Propagationcited by 2
  24. 2022Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methodscited by 2
  25. 2022Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluationcited by 2
  26. 2022Responsibility: An Example-based Explainable AI approach via Training Process Inspectioncited by 2
  27. 2023Interpreting Deep Neural Networks with the Package innsightcited by 2
  28. 2023DCNFIS: Deep Convolutional Neuro-Fuzzy Inference Systemcited by 2
  29. 2021Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interactioncited by 1
  30. 2021VitrAI -- Applying Explainable AI in the Real Worldcited by 1
  31. 2021Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive modelscited by 1
  32. 2021An XAI Approach to Deep Learning Models in the Detection of DCIScited by 1
  33. 2023Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learningcited by 1
  34. 2024CNN-based explanation ensembling for dataset, representation and explanations evaluationcited by 1
  35. 2024Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagationcited by 1
  36. 2025Learning phases with Quantum Monte Carlo simulation cellcited by 1
  37. 2022Robustness and Usefulness in AI Explanation Methodscited by 0
  38. 2022Future Artificial Intelligence tools and perspectives in medicinecited by 0

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