Search Results for author:
Found 10 papers, 7 papers with code
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans
Information from neuroimaging examinations (CT, MRI) is increasingly used to support diagnoses of dementia, e. g., Alzheimer's disease.
Feature Attribution Explanations for Spiking Neural Networks
We present \textit{Temporal Spike Attribution} (TSA), a local explanation method for SNNs.
Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN
In a recent paper, we introduced a \emph{worst-case} upper bound on how challenging morphing attacks can be for an FR system.
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers
Interpretable part-prototype models are computer vision models that are explainable by design.
Interpreting and Correcting Medical Image Classification with PIP-Net
We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging.
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification
Driven by the principle of explainability-by-design, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision.
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition.
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
By explaining such 'misleading' prototypes, we improve the interpretability and simulatability of a prototype-based classification model.
Causal Discovery with Attention-Based Convolutional Neural Networks
We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data.
