混合先验决策风险用于开放集识别

中文摘要 AI 辅助

在开放集识别（OSR）中，探针必须被识别为已知库类之一或被拒绝为未知，因此三种错误类型共存：误接受、误拒绝和误识别。用于选择性识别的不确定性分数应根据系统已做出的决策风险对探针进行排序。贝叶斯库感知模型，如整体不确定性估计（HolUE），通过Kullback-Leibler（KL）散度分量总结已知和未知类上的后验分布，并使用有监督的非线性校准器将其映射到不确定性分数。我们证明KL总结在决策风险上通常不是单调的：在验证数据上调优的KL分量线性融合在多个基准上产生负的过滤质量。我们提出MPRisk，一种混合先验后验决策风险分数，它保持相同的贝叶斯后验，但直接对与所选决策相关的错误事件进行评分：误接受、误识别和误拒绝风险，加上对拒绝的非特异性惩罚，通过将未知身份建模为连续分量实现。在验证集上调优的四个非负权重足以进行排序；不需要非线性监督模型。在九个图像、音频和文本基准上，MPRisk在图像和音频基准的每个操作点以及大多数文本操作点上达到最佳或并列最佳的预测拒绝比（PRR），在五个基准上通过bootstrap确认比HolUE有增益（最多+0.19 PRR），且运行时间相当或更低。

英文摘要

In open-set recognition (OSR), a probe must either be identified as one of the known gallery classes or rejected as unknown, so three error types coexist: false acceptance, false rejection, and misidentification. An uncertainty score for selective recognition should rank probes by the risk of the decision the system has made. Bayesian gallery-aware models such as Holistic Uncertainty Estimation (HolUE) summarize the posterior over known and unknown classes by Kullback--Leibler (KL) divergence components and map them to an uncertainty score with a supervised nonlinear calibrator. We show that the KL summary is not generally monotone in decision risk: linear fusion of the KL components tuned on validation data yields negative filtering quality on several benchmarks. We propose MPRisk, a mixed-prior posterior decision-risk score that keeps the same Bayesian posterior but directly scores the error events associated with the selected decision: false-acceptance, misidentification, and false-rejection risks, plus a non-specificity penalty for rejections, enabled by modeling unknown identities as a continuous component. Four nonnegative weights tuned on a validation set suffice for ranking; no nonlinear supervised model is required. Across nine image, audio, and text benchmarks, MPRisk achieves the best or tied-best Prediction Rejection Ratio at every operating point on the image and audio benchmarks and on most text operating points, with bootstrap-confirmed gains over HolUE on five benchmarks (up to $+0.19$ PRR) at comparable or lower runtime.

发表机构

• Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术学院（斯科尔科沃理工学院））
• Sber（俄罗斯联邦储蓄银行）
• The Institute for Information Transmission Problems (IITP RAS)（信息传输问题研究所（俄罗斯科学院信息传输问题研究所））

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