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model-specific-explanations

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rishiraajrs / Expanding-Knowledge-on-ML-Explainable-Models-for-Smarter-Decisions

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This repository presents a comprehensive research paper exploring the role of Explainable Artificial Intelligence (XAI) in modern Machine Learning. It aims to shed light on the interpretability of 'black-box' models like Neural Networks, Explainable AI and highlights the need for transparent, human-understandable ML systems.

knowledge-graph neural lime black-box-model xai interpretable-machine-learning shap counterfactual-explanations local-explanations post-hoc-analysis model-agnostic-explanations post-hoc-explanation global-explanation regulatory-compliance human-ai-interaction model-specific-explanations human-ai-collaboration transparency-trade-off performance-trade-off inherently-interpretable-models
  • Updated Aug 28, 2025

IDT-ITI / Text-XAI-Video-Summaries

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Code, models and data for our paper: Th. Eleftheriadis, E. Apostolidis, V. Mezaris, "An Experimental Study on Generating Plausible Textual Explanations for Video Summarization", IEEE CBMI 2025, Special Session on Explainability in Multimedia Analysis (ExMA)

video-summarization explainable-ai model-agnostic-explanations plausibility textual-explanations model-specific-explanations vision-language-models
  • Updated Sep 29, 2025
  • Python

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