Wellman, M.P.,
Henrion, M.,
Explaining 'explaining away',
PAMI(15), No. 3, March 1993, pp. 287-292.
IEEE DOI
0401
BibRef
Montavon, G.[Grégoire],
Lapuschkin, S.[Sebastian],
Binder, A.[Alexander],
Samek, W.[Wojciech],
Müller, K.R.[Klaus-Robert],
Explaining Nonlinear Classification Decisions with Deep Taylor
Decomposition,
PR(65), No. 1, 2017, pp. 211-222.
Elsevier DOI
1702
Award, Pattern Recognition. Deep neural networks
BibRef
Lapuschkin, S.,
Binder, A.,
Montavon, G.[Grégoire],
Müller, K.R.[Klaus-Robert],
Samek, W.[Wojciech],
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,
CVPR16(2912-2920)
IEEE DOI
1612
BibRef
Jung, A.,
Nardelli, P.H.J.,
An Information-Theoretic Approach to Personalized Explainable Machine
Learning,
SPLetters(27), 2020, pp. 825-829.
IEEE DOI
2006
Predictive models, Data models, Probabilistic logic,
Machine learning, Decision making, Linear regression,
decision support systems
BibRef
Muñoz-Romero, S.[Sergio],
Gorostiaga, A.[Arantza],
Soguero-Ruiz, C.[Cristina],
Mora-Jiménez, I.[Inmaculada],
Rojo-Álvarez, J.L.[José Luis],
Informative variable identifier:
Expanding interpretability in feature selection,
PR(98), 2020, pp. 107077.
Elsevier DOI
1911
Feature selection, Interpretability,
Explainable machine learning, Resampling, Classification
BibRef
Kauffmann, J.[Jacob],
Müller, K.R.[Klaus-Robert],
Montavon, G.[Grégoire],
Towards explaining anomalies:
A deep Taylor decomposition of one-class models,
PR(101), 2020, pp. 107198.
Elsevier DOI
2003
Outlier detection, Explainable machine learning,
Deep Taylor decomposition, Kernel machines, Unsupervised learning
BibRef
Yeom, S.K.[Seul-Ki],
Seegerer, P.[Philipp],
Lapuschkin, S.[Sebastian],
Binder, A.[Alexander],
Wiedemann, S.[Simon],
Müller, K.R.[Klaus-Robert],
Samek, W.[Wojciech],
Pruning by Explaining:
A Novel Criterion for Deep Neural Network Pruning,
PR(115), 2021, pp. 107899.
Elsevier DOI
2104
Pruning, Layer-wise relevance propagation (LRP),
Convolutional neural network (CNN), Interpretation of models,
Explainable AI (XAI)
BibRef
Pierrard, R.[Régis],
Poli, J.P.[Jean-Philippe],
Hudelot, C.[Céline],
Spatial relation learning for explainable image classification and
annotation in critical applications,
AI(292), 2021, pp. 103434.
Elsevier DOI
2102
Explainable artificial intelligence, Relation learning, Fuzzy logic
BibRef
Langer, M.[Markus],
Oster, D.[Daniel],
Speith, T.[Timo],
Hermanns, H.[Holger],
Kästner, L.[Lena],
Schmidt, E.[Eva],
Sesing, A.[Andreas],
Baum, K.[Kevin],
What do we want from Explainable Artificial Intelligence (XAI)?:
A stakeholder perspective on XAI and a conceptual model guiding
interdisciplinary XAI research,
AI(296), 2021, pp. 103473.
Elsevier DOI
2106
Explainable Artificial Intelligence, Explainability,
Interpretability, Explanations, Understanding,
Human-Computer Interaction
BibRef
Rio-Torto, I.[Isabel],
Fernandes, K.[Kelwin],
Teixeira, L.F.[Luís F.],
Understanding the decisions of CNNs: An in-model approach,
PRL(133), 2020, pp. 373-380.
Elsevier DOI
2005
Explainable AI, Explainability, Interpretability,
Deep Llearning, Convolutional Nneural Nnetworks
BibRef
Mokoena, T.[Tshepiso],
Celik, T.[Turgay],
Marivate, V.[Vukosi],
Why is this an anomaly? Explaining anomalies using sequential
explanations,
PR(121), 2022, pp. 108227.
Elsevier DOI
2109
Outlier explanation, Sequential feature explanation,
Sequential explanation, Anomaly validation, Explainable AI
BibRef
Anjomshoae, S.[Sule],
Omeiza, D.[Daniel],
Jiang, L.[Lili],
Context-based image explanations for deep neural networks,
IVC(116), 2021, pp. 104310.
Elsevier DOI
2112
DNNs, Explainable AI, Contextual importance, Visual explanations
BibRef
Sattarzadeh, S.[Sam],
Sudhakar, M.[Mahesh],
Plataniotis, K.N.[Konstantinos N.],
SVEA: A Small-scale Benchmark for Validating the Usability of
Post-hoc Explainable AI Solutions in Image and Signal Recognition,
HTCV21(4141-4150)
IEEE DOI
2112
Performance evaluation, Visualization, Image recognition,
Correlation, Machine learning, Benchmark testing
BibRef
Teneggi, J.[Jacopo],
Luster, A.[Alexandre],
Sulam, J.[Jeremias],
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
BibRef
Chattopadhyay, A.[Aditya],
Slocum, S.[Stewart],
Haeffele, B.D.[Benjamin D.],
Vidal, R.[René],
Geman, D.[Donald],
Interpretable by Design:
Learning Predictors by Composing Interpretable Queries,
PAMI(45), No. 6, June 2023, pp. 7430-7443.
IEEE DOI
2305
Birds, Task analysis, Predictive models, Image color analysis,
Computational modeling, Vegetation, Shape, Explainable AI, information theory
BibRef
Teney, D.[Damien],
Peyrard, M.[Maxime],
Abbasnejad, E.[Ehsan],
Predicting Is Not Understanding: Recognizing and Addressing
Underspecification in Machine Learning,
ECCV22(XXIII:458-476).
Springer DOI
2211
BibRef
Sovatzidi, G.[Georgia],
Vasilakakis, M.D.[Michael D.],
Iakovidis, D.K.[Dimitris K.],
Automatic Fuzzy Graph Construction For Interpretable Image
Classification,
ICIP22(3743-3747)
IEEE DOI
2211
Image edge detection, Semantics, Machine learning,
Predictive models, Feature extraction,
Interpretability
BibRef
Chari, P.[Pradyumna],
Ba, Y.H.[Yun-Hao],
Athreya, S.[Shreeram],
Kadambi, A.[Achuta],
MIME:
Minority Inclusion for Majority Group Enhancement of AI Performance,
ECCV22(XIII:326-343).
Springer DOI
2211
WWW Link.
BibRef
Deng, A.[Ailin],
Li, S.[Shen],
Xiong, M.[Miao],
Chen, Z.[Zhirui],
Hooi, B.[Bryan],
Trust, but Verify:
Using Self-supervised Probing to Improve Trustworthiness,
ECCV22(XIII:361-377).
Springer DOI
2211
BibRef
Rymarczyk, D.[Dawid],
Struski, L.[Lukasz],
Górszczak, M.[Michal],
Lewandowska, K.[Koryna],
Tabor, J.[Jacek],
Zielinski, B.[Bartosz],
Interpretable Image Classification with Differentiable Prototypes
Assignment,
ECCV22(XII:351-368).
Springer DOI
2211
BibRef
Vandenhende, S.[Simon],
Mahajan, D.[Dhruv],
Radenovic, F.[Filip],
Ghadiyaram, D.[Deepti],
Making Heads or Tails:
Towards Semantically Consistent Visual Counterfactuals,
ECCV22(XII:261-279).
Springer DOI
2211
WWW Link.
BibRef
Kim, S.S.Y.[Sunnie S. Y.],
Meister, N.[Nicole],
Ramaswamy, V.V.[Vikram V.],
Fong, R.[Ruth],
Russakovsky, O.[Olga],
HIVE: Evaluating the Human Interpretability of Visual Explanations,
ECCV22(XII:280-298).
Springer DOI
2211
BibRef
Jacob, P.[Paul],
Zablocki, É.[Éloi],
Ben-Younes, H.[Hédi],
Chen, M.[Mickaël],
Pérez, P.[Patrick],
Cord, M.[Matthieu],
STEEX: Steering Counterfactual Explanations with Semantics,
ECCV22(XII:387-403).
Springer DOI
2211
BibRef
Machiraju, G.[Gautam],
Plevritis, S.[Sylvia],
Mallick, P.[Parag],
A Dataset Generation Framework for Evaluating Megapixel Image
Classifiers and Their Explanations,
ECCV22(XII:422-442).
Springer DOI
2211
BibRef
Kolek, S.[Stefan],
Nguyen, D.A.[Duc Anh],
Levie, R.[Ron],
Bruna, J.[Joan],
Kutyniok, G.[Gitta],
Cartoon Explanations of Image Classifiers,
ECCV22(XII:443-458).
Springer DOI
2211
BibRef
Motzkus, F.[Franz],
Weber, L.[Leander],
Lapuschkin, S.[Sebastian],
Measurably Stronger Explanation Reliability Via Model Canonization,
ICIP22(516-520)
IEEE DOI
2211
Location awareness, Deep learning, Visualization,
Current measurement, Neural networks, Network architecture
BibRef
Yang, G.[Guang],
Rao, A.[Arvind],
Fernandez-Maloigne, C.[Christine],
Calhoun, V.[Vince],
Menegaz, G.[Gloria],
Explainable AI (XAI) In Biomedical Signal and Image Processing:
Promises and Challenges,
ICIP22(1531-1535)
IEEE DOI
2211
Deep learning, Image segmentation, Special issues and sections,
Biological system modeling, Signal processing, Data models, Biomedical Data
BibRef
Paiss, R.[Roni],
Chefer, H.[Hila],
Wolf, L.B.[Lior B.],
No Token Left Behind: Explainability-Aided Image Classification and
Generation,
ECCV22(XII:334-350).
Springer DOI
2211
BibRef
Khorram, S.[Saeed],
Li, F.X.[Fu-Xin],
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
BibRef
Hepburn, A.[Alexander],
Santos-Rodriguez, R.[Raul],
Explainers in the Wild: Making Surrogate Explainers Robust to
Distortions Through Perception,
ICIP21(3717-3721)
IEEE DOI
2201
Training, Measurement, Image processing, Predictive models,
Distortion, Robustness, Explainability, surrogates, perception
BibRef
Palacio, S.[Sebastian],
Lucieri, A.[Adriano],
Munir, M.[Mohsin],
Ahmed, S.[Sheraz],
Hees, J.[Jörn],
Dengel, A.[Andreas],
XAI Handbook: Towards a Unified Framework for Explainable AI,
RPRMI21(3759-3768)
IEEE DOI
2112
Measurement, Terminology, Pipelines,
Market research, Concrete
BibRef
Vierling, A.[Axel],
James, C.[Charu],
Berns, K.[Karsten],
Katsaouni, N.[Nikoletta],
Provable Translational Robustness for Object Detection With
Convolutional Neural Networks,
ICIP21(694-698)
IEEE DOI
2201
Training, Support vector machines, Analytical models, Scattering,
Object detection, Detectors, Feature extraction, Explainable AI
BibRef
Ortega, A.[Alfonso],
Fierrez, J.[Julian],
Morales, A.[Aythami],
Wang, Z.L.[Zi-Long],
Ribeiro, T.[Tony],
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair
and Explainable Automatic Recruitment,
WACVW21(78-87) Explainable and Interpretable AI
IEEE DOI
2105
Training, Machine learning algorithms,
Biometrics (access control), Resumes, Neural networks, Tools
BibRef
Kwon, H.J.[Hyuk Jin],
Koo, H.I.[Hyung Il],
Cho, N.I.[Nam Ik],
Improving Explainability of Integrated Gradients with Guided
Non-Linearity,
ICPR21(385-391)
IEEE DOI
2105
Measurement, Heating systems, Visualization, Gradient methods,
Action potentials, Perturbation methods, Neurons
BibRef
Fuhl, W.[Wolfgang],
Rong, Y.[Yao],
Motz, T.[Thomas],
Scheidt, M.[Michael],
Hartel, A.[Andreas],
Koch, A.[Andreas],
Kasneci, E.[Enkelejda],
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
BibRef
Mänttäri, J.[Joonatan],
Broomé, S.[Sofia],
Folkesson, J.[John],
Kjellström, H.[Hedvig],
Interpreting Video Features: A Comparison of 3d Convolutional Networks
and Convolutional LSTM Networks,
ACCV20(V:411-426).
Springer DOI
2103
See also Interpretable Explanations of Black Boxes by Meaningful Perturbation.
BibRef
Oussalah, M.[Mourad],
Ai Explainability. A Bridge Between Machine Vision and Natural Language
Processing,
EDL-AI20(257-273).
Springer DOI
2103
BibRef
Petkovic, D.,
Alavi, A.,
Cai, D.,
Wong, M.,
Random Forest Model and Sample Explainer for Non-experts in Machine
Learning: Two Case Studies,
EDL-AI20(62-75).
Springer DOI
2103
BibRef
Muddamsetty, S.M.[Satya M.],
Jahromi, M.N.S.[Mohammad N. S.],
Moeslund, T.B.[Thomas B.],
Expert Level Evaluations for Explainable Ai (XAI) Methods in the
Medical Domain,
EDL-AI20(35-46).
Springer DOI
2103
BibRef
Muddamsetty, S.M.,
Mohammad, N.S.J.,
Moeslund, T.B.,
SIDU: Similarity Difference And Uniqueness Method for Explainable AI,
ICIP20(3269-3273)
IEEE DOI
2011
Visualization, Predictive models, Machine learning,
Computational modeling, Measurement, Task analysis, Explainable AI, CNN
BibRef
Sun, Y.C.[You-Cheng],
Chockler, H.[Hana],
Huang, X.W.[Xiao-Wei],
Kroening, D.[Daniel],
Explaining Image Classifiers Using Statistical Fault Localization,
ECCV20(XXVIII:391-406).
Springer DOI
2011
BibRef
Choi, H.,
Som, A.,
Turaga, P.,
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability
in Image Classification,
Diff-CVML20(3659-3666)
IEEE DOI
2008
Training, Task analysis, Feature extraction, Euclidean distance,
Airplanes, Media
BibRef
Parafita, Á.,
Vitrià, J.,
Explaining Visual Models by Causal Attribution,
VXAI19(4167-4175)
IEEE DOI
2004
data handling, feature extraction, intervened causal model,
causal attribution, visual models, image generative models,
learning
BibRef
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
BibRef
Fong, R.C.[Ruth C.],
Vedaldi, A.[Andrea],
Interpretable Explanations of Black Boxes by Meaningful Perturbation,
ICCV17(3449-3457)
IEEE DOI
1802
Explain the result of learning.
image classification, learning (artificial intelligence),
black box algorithm, black boxes, classifier decision,
Visualization
BibRef
Hossam, M.[Mahmoud],
Le, T.[Trung],
Zhao, H.[He],
Phung, D.[Dinh],
Explain2Attack: Text Adversarial Attacks via Cross-Domain
Interpretability,
ICPR21(8922-8928)
IEEE DOI
2105
Training, Deep learning, Computational modeling,
Perturbation methods, Text categorization, Natural languages, Training data
BibRef
Plummer, B.A.[Bryan A.],
Vasileva, M.I.[Mariya I.],
Petsiuk, V.[Vitali],
Saenko, K.[Kate],
Forsyth, D.A.[David A.],
Why Do These Match? Explaining the Behavior of Image Similarity Models,
ECCV20(XI:652-669).
Springer DOI
2011
BibRef
Cheng, X.,
Rao, Z.,
Chen, Y.,
Zhang, Q.,
Explaining Knowledge Distillation by Quantifying the Knowledge,
CVPR20(12922-12932)
IEEE DOI
2008
Visualization, Task analysis, Measurement, Knowledge engineering,
Optimization, Entropy, Neural networks
BibRef
Chen, Y.,
Nonparametric Learning Via Successive Subspace Modeling (SSM),
ICIP19(3031-3032)
IEEE DOI
1910
Machine Learning, Explainable Machine Learning,
Nonparametric Learning, Subspace Modeling, Successive Subspace Modeling
BibRef
Shi, J.X.[Jia-Xin],
Zhang, H.W.[Han-Wang],
Li, J.Z.[Juan-Zi],
Explainable and Explicit Visual Reasoning Over Scene Graphs,
CVPR19(8368-8376).
IEEE DOI
2002
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Constraint Based Matching .