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Found 4 papers, 2 papers with code
Contextualised Out-of-Distribution Detection using Pattern Identication
In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring patterns to build a robust Out-of-Distribution (OoD) detection method for visual classifiers.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Interpretable Out-Of-Distribution Detection Using Pattern Identification
Out-of-distribution (OoD) detection for data-based programs is a goal of paramount importance.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Trustworthiness Score to Evaluate DNN Predictions
Due to the black box nature of deep neural networks (DNN), the continuous validation of DNN during operation is challenging with the absence of a human monitor.
DIRA: Dynamic Domain Incremental Regularised Adaptation
We show that DIRA improves on the problem of forgetting and achieves strong gains in performance when retraining using a few samples from the target domain.
