A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.
For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution
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Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.