CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring
CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring explores A novel OOD detection method that combines classifier confidence with a residual signal to achieve robust performance across various architectures and datasets.. Commercial viability score: 7/10 in AI Safety.
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