mlsanityData to 14 Oct 2026

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

arXiv 1911.12116 ↗First posted 27 Nov 2019cs.AI cs.CVcited by 93

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Abstract (arXiv)

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized to be non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificial datasets, often with bias or contaminated discriminating content. Through their increased distribution, decision-making algorithms can contribute promoting prejudge and unfairness which is not easy to notice due to lack of transparency. Hence, scientists developed several so-called explanators or explainers which try to point out the connection between input and output to represent in a simplified way the inner structure of machine learning black boxes. In this survey we differ the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

Key facts. Cited by 93 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2019.

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Citing papers per year: 2021: 18; 2022: 14; 2023: 25; 2024: 16; 2025: 14; 2026: 6.

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  1. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 724
  2. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  3. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  4. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  5. 2022From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectorscited by 83
  6. 2022ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognitioncited by 63
  7. 2023Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomycited by 51
  8. 2024TimeX++: Learning Time-Series Explanations with Information Bottleneckcited by 33
  9. 2021EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use casecited by 30
  10. 2022Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geosciencecited by 28
  11. 2023What do neural networks learn in image classification? A frequency shortcut perspectivecited by 28
  12. 2023CosPGD: an efficient white-box adversarial attack for pixel-wise prediction taskscited by 27
38 more
  1. 2022A Survey of Adversarial Defences and Robustness in NLPcited by 23
  2. 2021Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Datasetcited by 18
  3. 2021Explaining Bayesian Neural Networkscited by 18
  4. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  5. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  6. 2023A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysiscited by 15
  7. 2024Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agentcited by 15
  8. 2022DORA: Exploring Outlier Representations in Deep Neural Networkscited by 14
  9. 2022Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationcited by 12
  10. 2023Interpretability of Machine Learning: Recent Advances and Future Prospectscited by 12
  11. 2025Do Large Language Models Know How Much They Know?cited by 11
  12. 2024AI-enhanced Collective Intelligencecited by 10
  13. 2023Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approachescited by 9
  14. 2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
  15. 2022GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifscited by 6
  16. 2023Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objectivecited by 6
  17. 2023Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanationscited by 6
  18. 2024Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Modelscited by 6
  19. 2024Identifying Bias in Deep Neural Networks Using Image Transformscited by 6
  20. 2022Survey on Applications of Neurosymbolic Artificial Intelligencecited by 5
  21. 2022Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generationcited by 5
  22. 2021Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 imagescited by 4
  23. 2023Number Systems for Deep Neural Network Architectures: A Surveycited by 4
  24. 2025SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligencecited by 4
  25. 2021Recurrent Localization Networks applied to the Lippmann-Schwinger Equationcited by 3
  26. 2021What's wrong with this video? Comparing Explainers for Deepfake Detectioncited by 3
  27. 2023sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRIcited by 3
  28. 2023Morphological Profiling for Drug Discovery in the Era of Deep Learningcited by 3
  29. 2024Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applicationscited by 3
  30. 2024Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosiscited by 3
  31. 2021CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicingcited by 2
  32. 2021Scope and Sense of Explainability for AI-Systemscited by 2
  33. 2022Explainable Reinforcement Learning on Financial Stock Trading using SHAPcited by 2
  34. 2024Conformal Depression Predictioncited by 2
  35. 2021Dominant motion identification of multi-particle system using deep learning from videocited by 1
  36. 2021An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Diseasecited by 1
  37. 2021CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Imagescited by 1
  38. 2021Does Explainable Machine Learning Uncover the Black Box in Vision Applications?cited by 1

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