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Found 5 papers, 1 papers with code
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project.
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
However, selecting data for annotation remains a challenging problem due to the limited information available on unseen data.
Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency
Ensuring the trustworthiness and interpretability of machine learning models is critical to their deployment in real-world applications.
Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust
In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the classification.
