A multi-encoder fusion of representation-space diffusion models via EncMin2L and Tippett minimum p-value combination detects OOD across global, semantic, texture, and corruption shifts with >=0.94 AUROC at reduced parameter cost.
arXiv preprint arXiv:2508.15737 , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
A score-based diffusion model estimates joint likelihoods of inputs and regression predictions to detect out-of-distribution cases in scientific tasks, with the likelihood correlating to prediction error.
citing papers explorer
-
Tippett-minimum Fusion of Representation-space Diffusion Models for Multi-Encoder Out-of-Distribution Detection
A multi-encoder fusion of representation-space diffusion models via EncMin2L and Tippett minimum p-value combination detects OOD across global, semantic, texture, and corruption shifts with >=0.94 AUROC at reduced parameter cost.
-
Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
A score-based diffusion model estimates joint likelihoods of inputs and regression predictions to detect out-of-distribution cases in scientific tasks, with the likelihood correlating to prediction error.