mlsanityData to 7 Oct 2026

Xplique: A Deep Learning Explainability Toolbox

arXiv 2206.04394 ↗First posted 9 Jun 2022cs.LG cs.AIcited by 24

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Abstract (arXiv)

Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing their internal states or by studying the underlying data representation. To address this challenge, we have developed Xplique: a software library for explainability which includes representative explainability methods as well as associated evaluation metrics. It interfaces with one of the most popular learning libraries: Tensorflow as well as other libraries including PyTorch, scikit-learn and Theano. The code is licensed under the MIT license and is freely available at github.com/deel-ai/xplique.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 37 of its 51 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

37 of 51 references.

References · 37

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  5. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  6. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  7. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  8. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  9. 2018Sanity Checks for Saliency Mapscited by 1,149
  10. 2020Concept Bottleneck Modelscited by 1,022
  11. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  12. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
25 more
  1. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  2. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  3. 2020Captum: A unified and generic model interpretability library for PyTorchcited by 542
  4. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  5. 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 467
  6. 2017Interpretation of Neural Networks is Fragilecited by 443
  7. 2019On the (In)fidelity and Sensitivity for Explanationscited by 309
  8. 2019On Completeness-aware Concept-Based Explanations in Deep Neural Networkscited by 239
  9. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  10. 2020Evaluating and Aggregating Feature-based Model Explanationscited by 185
  11. 2019Sanity Checks for Saliency Metricscited by 119
  12. 2016Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networkscited by 106
  13. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  14. 2019When Explanations Lie: Why Many Modified BP Attributions Failcited by 92
  15. 2019An Evaluation of the Human-Interpretability of Explanationcited by 71
  16. 2019Understanding Neural Networks via Feature Visualization: A surveycited by 65
  17. 2021Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysiscited by 65
  18. 2020Evaluations and Methods for Explanation through Robustness Analysiscited by 51
  19. 2020IROF: a low resource evaluation metric for explanation methodscited by 45
  20. 2022Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysiscited by 38
  21. 2021DISSECT: Disentangled Simultaneous Explanations via Concept Traversalscited by 37
  22. 2019Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithmscited by 32
  23. 2022Human-Centered Concept Explanations for Neural Networkscited by 23
  24. 2021Best of both worlds: local and global explanations with human-understandable conceptscited by 22
  25. 2018Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivativecited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 6; 2025: 7; 2026: 3.

24 papers.

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  1. 2022CRAFT: Concept Recursive Activation FacTorization for Explainabilitycited by 177
  2. 2023A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimationcited by 108
  3. 2025Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Modelscited by 67
  4. 2022Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measurecited by 33
  5. 2023Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimizationcited by 26
  6. 2024Benchmarking XAI Explanations with Human-Aligned Evaluationscited by 8
  7. 2023Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanationscited by 6
  8. 2025Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimatorscited by 6
  9. 2023Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domaincited by 5
  10. 2025Representational Similarity via Interpretable Visual Conceptscited by 5
  11. 2026FAME: Formal Abstract Minimal Explanation for Neural Networkscited by 3
  12. 2023DCNFIS: Deep Convolutional Neuro-Fuzzy Inference Systemcited by 2
12 more
  1. 2024Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyondcited by 2
  2. 2025A Survey on Interpretability in Visual Recognitioncited by 2
  3. 2023Deep Natural Language Feature Learning for Interpretable Predictioncited by 1
  4. 2025Measuring the (Un)Faithfulness of Concept-Based Explanationscited by 1
  5. 2025Interpreto: An Explainability Library for Transformerscited by 1
  6. 2025Back to the Baseline: Examining Baseline Effects on Explainability Metricscited by 1
  7. 2024NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniquescited by 0
  8. 2024Unlearning-based Neural Interpretationscited by 0
  9. 2024XAI-based Feature Selection for Improved Network Intrusion Detection Systemscited by 0
  10. 2024PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imagingcited by 0
  11. 2026The Impact of Lesion Focus on the Performance of AI-Based Melanoma Classificationcited by 0
  12. 2026Building Trust in Artificial Intelligence: A Necessity for Railway Applicationscited by 0