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Predictive models, Data models, Probabilistic logic,
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Outlier detection, Explainable machine learning,
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Pruning, Layer-wise relevance propagation (LRP),
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Explainable artificial intelligence, Relation learning, Fuzzy logic
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What do we want from Explainable Artificial Intelligence (XAI)?:
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2106
Explainable Artificial Intelligence, Explainability,
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2005
Explainable AI, Explainability, Interpretability,
Deep Llearning, Convolutional Nneural Nnetworks
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Why is this an anomaly? Explaining anomalies using sequential
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2109
Outlier explanation, Sequential feature explanation,
Sequential explanation, Anomaly validation, Explainable AI
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2112
DNNs, Explainable AI, Contextual importance, Visual explanations
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SVEA: A Small-scale Benchmark for Validating the Usability of
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HTCV21(4141-4150)
IEEE DOI
2112
Performance evaluation, Visualization, Image recognition,
Correlation, Machine learning, Benchmark testing
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Zhang, Q.S.[Quan-Shi],
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Quantifying the Knowledge in a DNN to Explain Knowledge Distillation
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PAMI(45), No. 4, April 2023, pp. 5099-5113.
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2303
Knowledge engineering, Task analysis, Measurement, Optimization,
Feature extraction, Birds, Visualization, Knowledge distillation,
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Fast Hierarchical Games for Image Explanations,
PAMI(45), No. 4, April 2023, pp. 4494-4503.
IEEE DOI
2303
Games, Computational modeling, Neural networks, Tumors,
Task analysis, Supervised learning, Standards,
image explanations
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Chattopadhyay, A.[Aditya],
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Interpretable by Design:
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PAMI(45), No. 6, June 2023, pp. 7430-7443.
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2305
Birds, Task analysis, Predictive models, Image color analysis,
Computational modeling, Vegetation, Shape, Explainable AI, information theory
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Bandyapadhyay, S.[Sayan],
Fomin, F.V.[Fedor V.],
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How to find a good explanation for clustering?,
AI(322), 2023, pp. 103948.
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2308
Explainable clustering, Clustering with outliers, Multivariate analysis
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Chen, H.F.[Hai-Fei],
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Enhancing Land Cover Mapping and Monitoring: An Interactive and
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DTEC: Decision tree-based evidential clustering for interpretable
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PR(144), 2023, pp. 109846.
Elsevier DOI
2310
Evidential clustering, Interpretable clustering,
Unsupervised decision tree, Belief function theory
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Geospatial XAI: A Review,
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Explainable Deep Learning Methods in Medical Image Classification: A
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Surveys(56), No. 4, October 2023, pp. xx-yy.
DOI Link
2312
Survey, Explainable AI. Explainable AI, interpretability, explainability,
deep learning, medical image analysis
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Counterfactual explanations for misclassified images: How human and
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AI(324), 2023, pp. 103995.
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2312
XAI, Counterfactual explanation, User testing
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ECLAD: Extracting Concepts with Local Aggregated Descriptors,
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Elsevier DOI
2312
Concept extraction, Explainable artificial intelligence,
Convolutional neural networks
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Liu, P.[Peng],
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Unlocking the Potential of Explainable Artificial Intelligence in
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PRIME: Posterior Reconstruction of the Input for Model Explanations,
PRL(176), 2023, pp. 202-208.
Elsevier DOI
2312
Machine learning, Statistical inference, Classification, Model explainability
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Wang, Z.[Zhuo],
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Learning Interpretable Rules for Scalable Data Representation and
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PAMI(46), No. 2, February 2024, pp. 1121-1133.
IEEE DOI
2401
Interpretable classification, representation learning,
rule-based model, scalability
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Rong, Y.[Yao],
Leemann, T.[Tobias],
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Fiedler, L.[Lisa],
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Kasneci, G.[Gjergji],
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Towards Human-Centered Explainable AI:
A Survey of User Studies for Model Explanations,
PAMI(46), No. 4, April 2024, pp. 2104-2122.
IEEE DOI
2403
Artificial intelligence, Task analysis,
Human computer interaction, Surveys, Bibliographies, Usability,
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On the coherency of quantitative evaluation of visual explanations,
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Elsevier DOI
2403
Deep learning, Explainable AI (XAI), Visual explanations
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Fast and explainable clustering based on sorting,
PR(150), 2024, pp. 110298.
Elsevier DOI
2403
Clustering, Fast aggregation, Sorting, Explainability
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Wulfert, L.[Lars],
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Grabmaier, A.[Anton],
AIfES: A Next-Generation Edge AI Framework,
PAMI(46), No. 6, June 2024, pp. 4519-4533.
IEEE DOI
2405
Training, Data models, Artificial intelligence,
Support vector machines, Hardware acceleration, Libraries,
TinyML
BibRef
Pelous, E.[Enzo],
Méger, N.[Nicolas],
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Atto, A.[Abdourrahmane],
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Courteille, H.[Hermann],
Lin-Kwong-Chon, C.[Christophe],
Explaining the decisions and the functioning of a convolutional
spatiotemporal land cover classifier with channel attention and
redescription mining,
PandRS(215), 2024, pp. 256-270.
Elsevier DOI
2408
Explainable AI, Convolutional neural networks,
Land cover classification, Satellite image time series,
Grouped frequent sequential patterns
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Shang, Q.J.[Qi-Jie],
Zheng, T.[Tieran],
Zhang, L.W.[Li-Wen],
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Concept-Based Explanations for Millimeter Wave Radar Target
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RS(16), No. 14, 2024, pp. 2640.
DOI Link
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Opti-CAM: Optimizing saliency maps for interpretability,
CVIU(248), 2024, pp. 104101.
Elsevier DOI
2409
Interpretability, Explainable AI, Saliency map,
Class activation maps, Computer vision
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Tasneem, S.[Sumaiya],
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Improve Adversarial Robustness of AI Models in Remote Sensing via
Data-Augmentation and Explainable-AI Methods,
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DOI Link
2409
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Li, Y.S.[Yan-Shan],
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WB-LRP: Layer-wise relevance propagation with weight-dependent
baseline,
PR(158), 2025, pp. 110956.
Elsevier DOI
2411
Layer-Wise Relevance Propagation (LRP), Interpretation
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Kuznietsov, A.[Anton],
Gyevnar, B.[Balint],
Wang, C.[Cheng],
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Albrecht, S.V.[Stefano V.],
Explainable AI for Safe and Trustworthy Autonomous Driving: A
Systematic Review,
ITS(25), No. 12, December 2024, pp. 19342-19364.
IEEE DOI
2412
Safety, Surveys, Explainable AI, Stakeholders, Planning, Taxonomy,
Standards, Monitoring, Autonomous vehicles, Terminology, AI safety
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Jeanneret, G.[Guillaume],
Simon, L.[Loďc],
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Diffusion Models for Counterfactual Explanations,
CVIU(249), 2024, pp. 104207.
Elsevier DOI
2412
Counterfactual explanations, Explainable AI, Diffusion models,
Spurious correlation detection
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Sun, T.L.[Tian-Li],
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Hu, G.S.[Guo-Sheng],
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Explainability-based knowledge distillation,
PR(159), 2025, pp. 111095.
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2412
Explainability, Knowledge distillation, CAM
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Explaining predictive uncertainty by exposing second-order effects,
PR(160), 2025, pp. 111171.
Elsevier DOI
2501
Explainable AI, Predictive uncertainty, Ensemble models,
Second-order attribution
BibRef
Bello, M.[Marilyn],
Amador, R.[Rosalís],
García, M.M.[María-Matilde],
del Ser, J.[Javier],
Mesejo, P.[Pablo],
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The level of strength of an explanation: A quantitative evaluation
technique for post-hoc XAI methods,
PR(161), 2025, pp. 111221.
Elsevier DOI
2502
Explainable artificial intelligence, Trustworthiness,
Feature attribution, Quantitative evaluation, Likelihood ratio
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Sreedharan, S.[Sarath],
Srivastava, S.[Siddharth],
Kambhampati, S.[Subbarao],
Explain it as simple as possible, but no simpler: Explanation via
model simplification for addressing inferential gap,
AI(340), 2025, pp. 104279.
Elsevier DOI
2502
Explanations for plans, Abstractions, Contrastive explanations
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Hu, L.[Lianyu],
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Liu, X.[Xinying],
He, Z.[Zengyou],
Interpretable categorical data clustering via hypothesis testing,
PR(162), 2025, pp. 111364.
Elsevier DOI
2503
Interpretable clustering, Categorical data,
Binary decision trees, Statistical hypothesis test, Splitting criteria
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Xu, A.[Ao],
Li, Z.[Zihao],
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Wu, T.[Tieru],
Generating Image Counterfactuals in Deep Learning Models Without the
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SPLetters(32), 2025, pp. 1495-1499.
IEEE DOI
2504
Signal processing algorithms, Artificial intelligence,
Closed box, Deep learning, Vectors, Optimization, Data models,
image counterfactual explanation
BibRef
Zhou, P.[Peng],
Tong, Q.H.[Qi-Hui],
Chen, S.[Shiji],
Zhang, Y.Y.[Yun-Yun],
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EACE: Explain Anomaly via Counterfactual Explanations,
PR(164), 2025, pp. 111532.
Elsevier DOI
2504
Interpretable machine learning, Counterfactual explanation,
Anomaly detection, Genetic algorithm
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Choi, H.[Hoyoung],
Jin, S.[Seungwan],
Han, K.[Kyungsik],
ICEv2: Interpretability, Comprehensiveness, and Explainability in
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IJCV(133), No. 5, May 2025, pp. 2487-2504.
Springer DOI
2504
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Miles, R.[Roy],
Elezi, I.[Ismail],
Deng, J.K.[Jian-Kang],
V_kD: Improving Knowledge Distillation Using Orthogonal Projections,
CVPR24(15720-15730)
IEEE DOI Code:
WWW Link.
2410
Training, Deep learning, Image synthesis, Object detection,
Transformer cores, Transformers, Knowledge distillation, Explainable AI
BibRef
Wang, H.J.[Han-Jing],
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Ji, Q.[Qiang],
Optimization-based Uncertainty Attribution Via Learning Informative
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ECCV24(LXXVIII: 237-253).
Springer DOI
2412
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Jabbour, S.[Sarah],
Kondas, G.[Gregory],
Kazerooni, E.[Ella],
Sjoding, M.[Michael],
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Depict: Diffusion-enabled Permutation Importance for Image
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ECCV24(LXIV: 35-51).
Springer DOI
2412
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Sobieski, B.[Bartlomiej],
Biecek, P.[Przemyslaw],
Global Counterfactual Directions,
ECCV24(LXIII: 72-90).
Springer DOI
2412
BibRef
Duan, J.R.[Jia-Rui],
Li, H.[Haoling],
Zhang, H.F.[Hao-Fei],
Jiang, H.[Hao],
Xue, M.Q.[Meng-Qi],
Sun, L.[Li],
Song, M.L.[Ming-Li],
Song, J.[Jie],
On the Evaluation Consistency of Attribution-based Explanations,
ECCV24(LXX: 206-224).
Springer DOI
2412
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Zhang, X.[Xianren],
Lee, D.[Dongwon],
Wang, S.[Suhang],
Comprehensive Attribution: Inherently Explainable Vision Model with
Feature Detector,
ECCV24(LXXXI: 196-213).
Springer DOI
2412
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Mehrpanah, A.[Amir],
Englesson, E.[Erik],
Azizpour, H.[Hossein],
On Spectral Properties of Gradient-based Explanation Methods,
ECCV24(LXXXVII: 282-299).
Springer DOI
2412
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Atote, B.[Bhushan],
Sanchez, V.[Victor],
Enhanced Prototypical Part Network (EPPNet) for Explainable Image
Classification Via Prototypes,
ICIP24(528-534)
IEEE DOI
2411
Accuracy, Prototypes, Artificial neural networks,
Artificial intelligence, Image classification, Explainable AI,
image classification
BibRef
Chowdhury, P.[Prithwijit],
Prabhushankar, M.[Mohit],
Al Regib, G.[Ghassan],
Deriche, M.[Mohamed],
Are Objective Explanatory Evaluation Metrics Trustworthy?
An Adversarial Analysis,
ICIP24(3938-3944)
IEEE DOI
2411
Measurement, Visualization, Shape, Shape measurement,
Neural networks, Reliability theory, Predictive models,
Importance Maps
BibRef
Gong, S.Z.[Shi-Zhan],
Dou, Q.[Qi],
Farnia, F.[Farzan],
Structured Gradient-Based Interpretations via Norm-Regularized
Adversarial Training,
CVPR24(11009-11018)
IEEE DOI Code:
WWW Link.
2410
Training, Perturbation methods, Benchmark testing,
Stability analysis, Robustness, adversarial training
BibRef
Bora, R.P.[Revoti Prasad],
Terhorst, P.[Philipp],
Veldhuis, R.[Raymond],
Ramachandra, R.[Raghavendra],
Raja, K.[Kiran],
SLICE: Stabilized LIME for Consistent Explanations for Image
Classification,
CVPR24(10988-10996)
IEEE DOI
2410
Training, Perturbation methods, Closed box, Feature extraction,
Artificial intelligence, LIME, XAI, Interpretability
BibRef
Lei, X.H.[Xiao-Han],
Wang, M.[Min],
Zhou, W.G.[Wen-Gang],
Li, L.[Li],
Li, H.Q.[Hou-Qiang],
Instance-Aware Exploration-Verification-Exploitation for Instance
ImageGoal Navigation,
CVPR24(16329-16339)
IEEE DOI Code:
WWW Link.
2410
Visualization, Solid modeling, Navigation, Decision making,
Semantics, Switches, Visual Navigation, Verification, Embodied Vision
BibRef
Wu, W.J.[Wen-Jun],
Zhang, L.L.[Ling-Ling],
Liu, J.[Jun],
Tang, X.[Xi],
Wang, Y.X.[Ya-Xian],
Wang, S.W.[Shao-Wei],
Wang, Q.Y.[Qian-Ying],
E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and
Bottom-Up Generator,
CVPR24(13828-13837)
IEEE DOI
2410
Geometry, Training, Annotations, Formal languages, Generators,
Cognition, Geometry Problem Solving
BibRef
Sumiyasu, K.[Kosuke],
Kawamoto, K.[Kazuhiko],
Kera, H.[Hiroshi],
Identifying Important Group of Pixels using Interactions,
CVPR24(6017-6026)
IEEE DOI Code:
WWW Link.
2410
Greedy algorithms, Visualization, Costs, Codes,
Computational modeling, Predictive models, explainable AI, Interactions
BibRef
Wang, B.S.[Bor-Shiun],
Wang, C.Y.[Chien-Yi],
Chiu, W.C.[Wei-Chen],
MCPNet: An Interpretable Classifier via Multi-Level Concept
Prototypes,
CVPR24(10885-10894)
IEEE DOI Code:
WWW Link.
2410
Training, Computational modeling, Semantics, Decision making,
Prototypes, Explainable AI, Multi-scale explanation
BibRef
Bandyopadhyay, H.[Hmrishav],
Chowdhury, P.N.[Pinaki Nath],
Bhunia, A.K.[Ayan Kumar],
Sain, A.[Aneeshan],
Xiang, T.[Tao],
Song, Y.Z.[Yi-Zhe],
What Sketch Explainability Really Means for Downstream Tasks?,
CVPR24(10997-11008)
IEEE DOI
2410
Adaptation models, Computational modeling
BibRef
Chakraborty, R.[Rwiddhi],
Sletten, A.[Adrian],
Kampffmeyer, M.C.[Michael C.],
ExMap: Leveraging Explainability Heatmaps for Unsupervised Group
Robustness to Spurious Correlations,
CVPR24(12017-12026)
IEEE DOI Code:
WWW Link.
2410
Heating systems, Deep learning, Training, Learning systems, Bridges, Correlation
BibRef
Berrouyne, M.[Mustapha],
Hami, H.[Hinde],
Jouilil, Y.[Youness],
Predictive Power of AI: Tackling Child Undernutrition in Morocco,
ISCV24(1-8)
IEEE DOI
2408
Logistic regression, Pediatrics, Sociology, Predictive models,
Nearest neighbor methods, Boosting, Prediction algorithms, Gradient Boosting
BibRef
Bachiri, K.[Khalil],
Malek, M.[Maria],
Yahyaouy, A.[Ali],
Rogovschi, N.[Nicoleta],
Adaptive Subgraph Feature Extraction for Explainable Multi-Modal
Learning,
ISCV24(1-7)
IEEE DOI
2408
Adaptation models, Adaptive systems, Social networking (online),
Decision making, Refining, Closed box, Feature extraction,
Adaptive Subgraph Feature Extraction
BibRef
Hong, J.Y.[Jin-Yung],
Park, K.H.[Keun Hee],
Pavlic, T.P.[Theodore P.],
Concept-Centric Transformers: Enhancing Model Interpretability
through Object-Centric Concept Learning within a Shared Global
Workspace,
WACV24(4868-4879)
IEEE DOI
2404
Computational modeling, Semantics, Decision making, Memory modules,
Transformers, Feature extraction, Synchronization, Algorithms, and algorithms
BibRef
Wan, Q.Y.[Qi-Yang],
Wang, R.P.[Rui-Ping],
Chen, X.L.[Xi-Lin],
Interpretable Object Recognition by Semantic Prototype Analysis,
WACV24(789-798)
IEEE DOI
2404
Visualization, Analytical models, Semantics, Natural languages,
Prototypes, Object recognition, Algorithms, ethical computer vision
BibRef
Wang, C.[Chong],
Liu, Y.[Yuyuan],
Chen, Y.H.[Yuan-Hong],
Liu, F.B.[Feng-Bei],
Tian, Y.[Yu],
McCarthy, D.[Davis],
Frazer, H.[Helen],
Carneiro, G.[Gustavo],
Learning Support and Trivial Prototypes for Interpretable Image
Classification,
ICCV23(2062-2072)
IEEE DOI
2401
BibRef
Santos, F.A.O.[Flávio Arthur Oliveira],
Zanchettin, C.[Cleber],
Exploring Image Classification Robustness and Interpretability with
Right for the Right Reasons Data Augmentation,
LXCV-ICCV23(4149-4158)
IEEE DOI
2401
BibRef
Zhang, Y.F.[Yi-Fei],
Gu, S.[Siyi],
Gao, Y.Y.[Yu-Yang],
Pan, B.[Bo],
Yang, X.F.[Xiao-Feng],
Zhao, L.[Liang],
MAGI: Multi-Annotated Explanation-Guided Learning,
ICCV23(1977-1987)
IEEE DOI
2401
BibRef
Fan, L.[Lei],
Liu, B.[Bo],
Li, H.X.[Hao-Xiang],
Wu, Y.[Ying],
Hua, G.[Gang],
Flexible Visual Recognition by Evidential Modeling of Confusion and
Ignorance,
ICCV23(1338-1347)
IEEE DOI
2401
Address confidence of results and multiple options.
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2309
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Image edge detection, Semantics, Machine learning,
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2211
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ICIP22(516-520)
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2211
Location awareness, Deep learning, Visualization,
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Explainable AI (XAI) In Biomedical Signal and Image Processing:
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ICIP22(1531-1535)
IEEE DOI
2211
Deep learning, Image segmentation, Special issues and sections,
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No Token Left Behind: Explainability-Aided Image Classification and
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ECCV22(XII:334-350).
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2211
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Cycle-Consistent Counterfactuals by Latent Transformations,
CVPR22(10193-10202)
IEEE DOI
2210
Try to find images similar to the query image that change the decision.
Training, Measurement, Visualization, Image resolution,
Machine vision, Computational modeling, Explainable computer vision
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Towards Black-Box Explainability with Gaussian Discriminant Knowledge
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SAIAD21(21-28)
IEEE DOI
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Visualization, Shape, Semantics, Training data, Object detection,
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Explainers in the Wild: Making Surrogate Explainers Robust to
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ICIP21(3717-3721)
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2201
Training, Measurement, Image processing, Predictive models,
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RPRMI21(3759-3768)
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Provable Translational Robustness for Object Detection With
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ICIP21(694-698)
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Training, Support vector machines, Analytical models, Scattering,
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Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair
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WACVW21(78-87) Explainable and Interpretable AI
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Training, Machine learning algorithms,
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Improving Explainability of Integrated Gradients with Guided
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ICPR21(385-391)
IEEE DOI
2105
Measurement, Heating systems, Visualization, Gradient methods,
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Explainable Online Validation of Machine Learning Models for
Practical Applications,
ICPR21(3304-3311)
IEEE DOI
2105
Machine learning algorithms, Microcontrollers, Memory management,
Data acquisition, Training data, Transforms, Machine learning
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Interpreting Video Features: A Comparison of 3d Convolutional Networks
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See also Interpretable Explanations of Black Boxes by Meaningful Perturbation.
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Ai Explainability. A Bridge Between Machine Vision and Natural Language
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EDL-AI20(257-273).
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Random Forest Model and Sample Explainer for Non-experts in Machine
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Expert Level Evaluations for Explainable Ai (XAI) Methods in the
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EDL-AI20(35-46).
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2103
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Moeslund, T.B.,
SIDU: Similarity Difference And Uniqueness Method for Explainable AI,
ICIP20(3269-3273)
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2011
Visualization, Predictive models, Machine learning,
Computational modeling, Measurement, Task analysis, Explainable AI, CNN
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Explaining Image Classifiers Using Statistical Fault Localization,
ECCV20(XXVIII:391-406).
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2011
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Training, Task analysis, Feature extraction, Euclidean distance,
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Parafita, Á.,
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Explaining Visual Models by Causal Attribution,
VXAI19(4167-4175)
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2004
data handling, feature extraction, intervened causal model,
causal attribution, visual models, image generative models,
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Schlegel, U.,
Arnout, H.,
El-Assady, M.,
Oelke, D.,
Keim, D.A.,
Towards A Rigorous Evaluation Of XAI Methods On Time Series,
VXAI19(4197-4201)
IEEE DOI
2004
image processing, learning (artificial intelligence),
text analysis, time series, SHAP, image domain, text-domain,
explainable-ai-evaluation
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Fong, R.C.[Ruth C.],
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Interpretable Explanations of Black Boxes by Meaningful Perturbation,
ICCV17(3449-3457)
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1802
Explain the result of learning.
image classification, learning (artificial intelligence),
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Le, T.[Trung],
Zhao, H.[He],
Phung, D.[Dinh],
Explain2Attack: Text Adversarial Attacks via Cross-Domain
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ICPR21(8922-8928)
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Training, Deep learning, Computational modeling,
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Vasileva, M.I.[Mariya I.],
Petsiuk, V.[Vitali],
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ECCV20(XI:652-669).
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CVPR20(12922-12932)
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Visualization, Task analysis, Measurement, Knowledge engineering,
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Nonparametric Learning Via Successive Subspace Modeling (SSM),
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1910
Machine Learning, Explainable Machine Learning,
Nonparametric Learning, Subspace Modeling, Successive Subspace Modeling
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Shi, J.X.[Jia-Xin],
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Explainable and Explicit Visual Reasoning Over Scene Graphs,
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Constraint Based Matching .