基于测试时间校准的双动态字典图域外分布检测
Graph Out-of-Distribution Detection via Test-Time Calibration with Dual Dynamic Dictionaries
AI总结：
BaCa通过双动态字典校准测试时间图域外分布检测，无需微调预训练模型，有效提升OOD检测性能。
摘要：
图域外分布（OOD）检测中的关键挑战在于训练过程中缺乏真实的OOD样本。现有方法通常被优化以捕捉分布内（ID）数据的特征并计算OOD分数，这往往限制了预训练模型表示分布边界的能力，导致不可靠的OOD检测。此外，图数据的潜在结构通常由多个底层因素决定，这一方面仍较少被探索。为了解决这些挑战，我们提出了一种新的测试时间图OOD检测方法，称为BaCa，该方法通过双动态更新的字典校准OOD分数，而无需微调预训练模型。具体而言，BaCa估计图核并仅使用测试样本应用混合策略以生成多样化的边界感知判别拓扑，从而避免暴露辅助数据集作为异常。我们通过优先队列和注意力机制构建双动态字典以自适应捕捉潜在的ID和OOD表示，这些表示随后用于边界感知的OOD分数校准。据我们所知，广泛的实验表明，BaCa在真实世界数据集上显著优于现有最先进方法的OOD检测性能。
英文摘要：
A key challenge in graph out-of-distribution (OOD) detection lies in the absence of ground-truth OOD samples during training. Existing methods are typically optimized to capture features within the in-distribution (ID) data and calculate OOD scores, which often limits pre-trained models from representing distributional boundaries, leading to unreliable OOD detection. Moreover, the latent structure of graph data is often governed by multiple underlying factors, which remains less explored. To address these challenges, we propose a novel test-time graph OOD detection method, termed BaCa, that calibrates OOD scores using dual dynamically updated dictionaries without requiring fine-tuning the pre-trained model. Specifically, BaCa estimates graphons and applies a mix-up strategy solely with test samples to generate diverse boundary-aware discriminative topologies, eliminating the need for exposing auxiliary datasets as outliers. We construct dual dynamic dictionaries via priority queues and attention mechanisms to adaptively capture latent ID and OOD representations, which are then utilized for boundary-aware OOD score calibration. To the best of our knowledge, extensive experiments on real-world datasets show that BaCa significantly outperforms existing state-of-the-art methods in OOD detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
