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ICIP22(516-520)
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Location awareness, Deep learning, Visualization,
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Try to find images similar to the query image that change the decision.
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ICIP21(694-698)
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2201
Training, Support vector machines, Analytical models, Scattering,
Object detection, Detectors, Feature extraction, Explainable AI
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WACVW21(78-87) Explainable and Interpretable AI
IEEE DOI
2105
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
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ICPR21(3304-3311)
IEEE DOI
2105
Machine learning algorithms, Microcontrollers, Memory management,
Data acquisition, Training data, Transforms, Machine learning
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2103
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).
Springer DOI
2103
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Random Forest Model and Sample Explainer for Non-experts in Machine
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2103
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Expert Level Evaluations for Explainable Ai (XAI) Methods in the
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2103
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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
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Explaining Image Classifiers Using Statistical Fault Localization,
ECCV20(XXVIII:391-406).
Springer DOI
2011
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Choi, H.,
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AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability
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Diff-CVML20(3659-3666)
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2008
Training, Task analysis, Feature extraction, Euclidean distance,
Airplanes, Media
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Parafita, Á.,
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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
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Hossam, M.[Mahmoud],
Le, T.[Trung],
Zhao, H.[He],
Phung, D.[Dinh],
Explain2Attack: Text Adversarial Attacks via Cross-Domain
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ICPR21(8922-8928)
IEEE DOI
2105
Training, Deep learning, Computational modeling,
Perturbation methods, Text categorization, Natural languages, Training data
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Plummer, B.A.[Bryan A.],
Vasileva, M.I.[Mariya I.],
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CVPR20(12922-12932)
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2008
Visualization, Task analysis, Measurement, Knowledge engineering,
Optimization, Entropy, Neural networks
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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
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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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2002
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Dataset Distillation, Dataset Summary, Dataset Quantization .