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Found 2 papers, 2 papers with code
Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks
When it comes to critical applications as autonomous driving, security and safety, medicine and health, the lack of interpretability of the network behavior tends to induce skepticism and limited trustworthiness, despite the accurate performance of such systems in the given task.
Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces
In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces.
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