IDT-ITI/P-TAME

Scripts and trained models from our paper: M. Ntrougkas, V. Mezaris, I. Patras, "P-TAME: Explain Any Image Classifier with Trained Perturbations", IEEE Open Journal of Signal Processing, 2025. DOI:10.1109/OJSP.2025.3568756.

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Oxid15/xai-benchmark

Open and extensible benchmark for XAI methods

Language: Python - Size: 1.78 MB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 6 - Forks: 0

a-fsh-r/IBO

IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology

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psyklopp/Dezible-com

🌀 Writing, documenting and sharing my journey in PhD. I am interested in the evaluation methods for XAI.

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lkopf/cosy

CoSy: Evaluating Textual Explanations

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SinaMohseni/Awesome-XAI-Evaluation

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

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ari-dasci/S-ReVel

Repository for ReVel framework to Measure Local-Linear Explanationsfor Black-Box Models

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dbdmg/Explainable-and-trustworthy-AI

Explainable and trustworthy AI Course

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Francesco-Sovrano/DoXpy

Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability".

Language: Python - Size: 1.49 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 7 - Forks: 2

JHoelli/Semantic-Meaningfulness

Semantic Meaningfulness: Evaluating counterfactual approaches for real world plausibility

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ggjay9/Application-Flow-Identification

Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.

Language: Jupyter Notebook - Size: 90.6 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

YuutoAiza/AutoDL_XAI

Research on AutoML and Explainability．

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AkashVS01/Covid-detection-using-XAI

CNN architectures Resnet-50 and InceptionV3 have been used to detect whether the CT scan images is covid affected or not and prediction is validated using explainable AI frameworks LIME and GradCAM.

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GhadaElkhawaga/ConsisXAI

ConsisXAI is an implementation of a technique to evaluate global machine learning explainability (XAI) methods based on feature subset consistency

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Enantiodromis/XAI_Classification

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