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At inference, anomalies are detected and localized via reconstruction residuals with respect to this subspace, yielding interpretable and statistically grounded anomaly scores. SubspaceAD reports state-of-the-art performance across one-shot and few-shot settings without memory banks, auxiliary datasets, prompt tuning, or any form of training.&Subspace Modeling: A PCA model is fit to these features to estimate the low-dimensional manifold of normal appearance. At inference time, anomalies are detected using the reconstruction residual with respect to this learned subspace. Despite its simplicity, SubspaceAD achieves state-of-the-art performance in one-shot and few-shot settings.`Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achieve strong results employing foundation-model features, but typically rely on memory banks, auxiliary datasets, or multi-modal tuning of vision-language models. We therefore question whether such complexity is necessary given the feature+Subspace Modeling: A PCA model is fit to these features to estimate the low-dimensional manifold of normal appearance. At inference time, anomalies are detected using the reconstruction residual with respect to this learned subspace. Despite its simplicity, SubspaceAD achieves state-of-the-art performance in one-shot and few-shot settings.!Subspace Modeling: A PCA model is fit to these features to estimate the low-dimensional manifold of normal appearance. At inference time, anomalies are detected using the reconstruction residual with respect to this learned subspace. Despite its simplicity, SubspaceAD achieves state-of-the-art performance in one-shot and few-shot settings.~SubSpace/Continuum is the longest-running massively multiplayer internet space combat game in the world. It's free and more addictive than crack* and we're looking for new blood.,Through comprehensive evaluations on MVTec-AD and VisA datasets, SubspaceAD outperforms state-of-the-art reconstruction-, memory-bank-, and VLM-based ap-proaches across all few-shot settings. SubspaceAD is interpretable and parameter-free, requir-ing only a single normal image per category and a single forward pass per test image.
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At inference, anomalies are detected and localized via reconstruction residuals with respect to this subspace, yielding interpretable and statistically grounded anomaly scores. SubspaceAD reports state-of-the-art performance across one-shot and few-shot settings without memory banks, auxiliary datasets, prompt tuning, or any form of training.&Subspace Modeling: A PCA model is fit to these features to estimate the low-dimensional manifold of normal appearance. At inference time, anomalies are detected using the reconstruction residual with respect to this learned subspace. Despite its simplicity, SubspaceAD achieves state-of-the-art performance in one-shot and few-shot settings.`Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achieve strong results employing foundation-model features, but typically rely on memory banks, auxiliary datasets, or multi-modal tuning of vision-language models. We therefore question whether such complexity is necessary given the feature+Subspace Modeling: A PCA model is fit to these features to estimate the low-dimensional manifold of normal appearance. At inference time, anomalies are detected using the reconstruction residual with respect to this learned subspace. Despite its simplicity, SubspaceAD achieves state-of-the-art performance in one-shot and few-shot settings.!Subspace Modeling: A PCA model is fit to these features to estimate the low-dimensional manifold of normal appearance. At inference time, anomalies are detected using the reconstruction residual with respect to this learned subspace. Despite its simplicity, SubspaceAD achieves state-of-the-art performance in one-shot and few-shot settings.~SubSpace/Continuum is the longest-running massively multiplayer internet space combat game in the world. It's free and more addictive than crack* and we're looking for new blood.,Through comprehensive evaluations on MVTec-AD and VisA datasets, SubspaceAD outperforms state-of-the-art reconstruction-, memory-bank-, and VLM-based ap-proaches across all few-shot settings. SubspaceAD is interpretable and parameter-free, requir-ing only a single normal image per category and a single forward pass per test image.