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Found 3 papers, 1 papers with code
TraCE: Trajectory Counterfactual Explanation Scores
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier.
Two-step counterfactual generation for OOD examples
Two fundamental requirements for the deployment of machine learning models in safety-critical systems are to be able to detect out-of-distribution (OOD) data correctly and to be able to explain the prediction of the model.
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection
We see that OOD samples tend to be classified into classes that have a distribution similar to the distribution of the entire dataset.
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