Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature relies on uncertainty quantification to evaluate the reliability of a learning model, which is particularly important for applications of out-of-distribution (OOD) detection and misclassification detection. We are interested in uncertainty quantification for interdependent node-level classification. We start our analysis based on graph posterior networks (GPNs) that optimize the uncertainty cross-entropy (UCE)-based loss function. We describe the theoretical limitations of the widely-used UCE loss. To alleviate the identified drawbacks, we propose a distance-based regularization that encourages clustered OOD nodes to remain clustered in the latent space. We conduct extensive comparison experiments on eight standard datasets and demonstrate that the proposed regularization outperforms the state-of-the-art in both OOD detection and misclassification detection.

翻译：深度神经网络在过去几十年取得了显著成功，但其校准性不佳，常产生不可靠的预测。大量文献依赖不确定性量化来评估学习模型的可靠性，这对于分布外检测和误分类检测等应用尤为重要。我们关注相互依赖的节点级分类中的不确定性量化问题。首先基于图后验网络展开分析，该网络优化基于不确定性交叉熵的损失函数。我们描述了广泛使用的UCE损失的理论局限性。为缓解已发现的缺陷，提出一种基于距离的正则化方法，促使聚类后的OOD节点在潜在空间中保持聚类。我们在八个标准数据集上进行了广泛的对比实验，结果表明所提出的正则化方法在OOD检测和误分类检测中均优于现有最优方法。