RADAR produces anomaly maps directly from attention-based diffusion models in a single forward pass, achieving higher F1 scores than reconstruction-based diffusion and statistical baselines on MVTec-AD and 3D-printed material data.
Candès, Xiaodong Li, Yi Ma, and John Wright
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A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
The paper proposes message-passing algorithms and a replica theory using cumulant expansion for tensor factorization inference in a dense limit on random graphs, avoiding Gaussian assumptions.
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Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
RADAR produces anomaly maps directly from attention-based diffusion models in a single forward pass, achieving higher F1 scores than reconstruction-based diffusion and statistical baselines on MVTec-AD and 3D-printed material data.
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Spectral Vision Transformer for Efficient Tokenization with Limited Data
A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
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Graphical model for factorization and completion of relatively high rank tensors by sparse sampling
The paper proposes message-passing algorithms and a replica theory using cumulant expansion for tensor factorization inference in a dense limit on random graphs, avoiding Gaussian assumptions.