papersTODAY 04:00 UTC
Lagrangian Sub-Flow Method Improves Out-of-Distribution Detection
Researchers propose a Lagrangian sub-flow framework built on continuous normalizing flows to detect out-of-distribution observations that lie in a subspace of high-dimensional data. The approach applies local diagnostics to the flow, aiming to better separate in-distribution samples from anomalous ones. The work is presented as a preprint on arXiv.