Cargando…
CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models
We propose CX-ToM, short for counterfactual explanations with theory-of-mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, we pose expla...
Autores principales: | Akula, Arjun R., Wang, Keze, Liu, Changsong, Saba-Sadiya, Sari, Lu, Hongjing, Todorovic, Sinisa, Chai, Joyce, Zhu, Song-Chun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753121/ https://www.ncbi.nlm.nih.gov/pubmed/35036861 http://dx.doi.org/10.1016/j.isci.2021.103581 |
Ejemplares similares
-
Model agnostic generation of counterfactual explanations for molecules
por: Wellawatte, Geemi P., et al.
Publicado: (2022) -
PreCoF: counterfactual explanations for fairness
por: Goethals, Sofie, et al.
Publicado: (2023) -
GANterfactual—Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning
por: Mertes, Silvan, et al.
Publicado: (2022) -
Machine Learning and Explainable Artificial Intelligence Using Counterfactual Explanations for Evaluating Posture Parameters
por: Dindorf, Carlo, et al.
Publicado: (2023) -
Counterfactual Explanation of Brain Activity Classifiers Using Image-To-Image Transfer by Generative Adversarial Network
por: Matsui, Teppei, et al.
Publicado: (2022)