Explanation Fidelity Evaluation
6개 벤치마크 · 논문 7편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations
Can local explanation techniques explain linear additive models?
Towards Better Understanding Attribution Methods
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Meaningful Data Sampling for a Faithful Local Explanation Method
Papers
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations
Post-hoc explanation techniques on graph neural networks (GNNs) provide economical solutions for opening the black-box graph models without model retraining. Many GNN explanation variants have achieved state-of-the-art e…
Explanation Fidelity EvaluationCan local explanation techniques explain linear additive models?
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores. Questions have been raised about the accuracy of the produced local additi…
Additive modelsExplanation Fidelity EvaluationFeature ImportanceImproving Interpretability via Regularization of Neural Activation Sensitivity
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to…
Adversarial RobustnessExplanation Fidelity EvaluationInterpretability Techniques for Deep LearningSensitivityTowards Better Understanding Attribution Methods
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most…
Explainable artificial intelligenceExplanation Fidelity EvaluationImage ClassificationInterpretable Machine LearningDeveloping a Fidelity Evaluation Approach for Interpretable Machine Learning
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency. Explainable AI (…
BIG-bench Machine LearningExplainable Artificial Intelligence (XAI)Explanation Fidelity EvaluationInterpretable Machine LearningEXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation
Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations. We address this issue by proposing a robust and intuitive …
Explainable artificial intelligenceExplainable ModelsExplanation Fidelity Evaluation