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Found 5 papers, 1 papers with code
Explainable Supervised Domain Adaptation
Domain adaptation techniques have contributed to the success of deep learning.
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them.
Evaluation of Saliency-based Explainability Method
A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as a way to explain its working.
MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers
Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined.
MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks
The current methods to explain the predictions of a pre-trained model rely on gradient information, often resulting in saliency maps that focus on the foreground object as a whole.
