SAGE uses simplex-anchored graph-state equipartition and equiangular tight frames to perform structural inference on representations, bypassing distribution estimation in universal semi-supervised learning and achieving 8.52% average accuracy gains on benchmarks.
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CoOD decomposes inputs into components and applies Component Shift Score plus Compositional Consistency Score to improve detection of both standard and compositional out-of-distribution data.
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Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning
SAGE uses simplex-anchored graph-state equipartition and equiangular tight frames to perform structural inference on representations, bypassing distribution estimation in universal semi-supervised learning and achieving 8.52% average accuracy gains on benchmarks.
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Component-Based Out-of-Distribution Detection
CoOD decomposes inputs into components and applies Component Shift Score plus Compositional Consistency Score to improve detection of both standard and compositional out-of-distribution data.