Author

Luke Luschwitz

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Preprint Jul 2026

Sparse Autoencoders for Interpretable Out-of-Distribution Detection

A novel approach that leverages sparse autoencoders (SAEs) to learn interpretable features from these intermediate activations and proposes a new OOD score derived from the cosine similarity between the sparse feature activations of a test sample and the mean activations of ID classes.

Ayush Karmacharya, Luke Luschwitz, Lucia Romero et al. · 0 citations