UAPA 提出不确定性感知原型学习，解决开放集测试时适应中的 csOOD 干扰
Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation
一篇论文提出 UAPA，专门解决开放集测试时适应里 OOD 样本污染训练的问题，在 CIFAR 和 Tiny-ImageNet-C 上 OSCR 提升约 1-2 个百分点，做 TTA 研究的可以看看。
UAPA 是一种即插即用的开放集测试时适应（TTA）方法，针对无标注测试流中协变量偏移的分布内（csID）与分布外（csOOD）样本共存的问题。它通过分类器-原型分数结合小批量归一化的不确定性分数来区分 csID 与 csOOD 样本，并周期性用缓冲的 csID 特征更新各类别原型。其对比原型熵适应对 csID 样本做熵最小化、对 csOOD 样本做熵最大化，同时用原型级对比学习对齐特征。在 CIFAR 基准和 Tiny-ImageNet-C 上，UAPA 的平均 OSCR 分别比最强基线提升 2.08 和 1.28 个百分点。
Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation
Open-set test-time adaptation (TTA) aims to adapt source-trained models to unlabeled test streams where covariate-shifted in-distribution (csID) samples and covariate-shifted out-of-distribution (csOOD) samples may coexist. Existing TTA methods are typically designed under a closed-set assumption and therefore lack explicit mechanisms for reliably distinguishing csID from csOOD samples. Consequently, csOOD samples may be mistakenly included in confidence-enhancing adaptation, producing harmful gradients that degrade known-class performance. To address this, we propose Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation (UAPA), a plug-and-play method for reliable open-set adaptation. Specifically, we first introduce Uncertainty-Driven ID/OOD Separation, which combines a classifier-prototype score with a mini-batch-normalized evidence-derived uncertainty score to distinguish csID from csOOD samples. We then propose a Dynamic Prototype Refinement strategy that periodically refines class-specific target prototypes using buffered csID features to progressively follow the estimated class directions. Lastly, we introduce Contrastive Prototype Entropy Adaptation, which applies entropy minimization to csID samples and entropy maximization to csOOD samples, while prototype-level contrastive learning aligns csID features with their corresponding target prototypes and reduces the maximum similarity between csOOD features and known-class prototypes. Empirically, UAPA outperforms the strongest baselines on the CIFAR benchmarks and Tiny-ImageNet-C, improving average OSCR by 2.08 and 1.28 percentage points, respectively.