A key feature of out-of-distribution (OOD) detection is to exploit a trained neural network by extracting statistical patterns and relationships through the multi-layer classifier to detect shifts in the expected input data distribution. Despite achieving solid results, several state-of-the-art methods rely on the penultimate or last layer outputs only, leaving behind valuable information for OOD detection. Methods that explore the multiple layers either require a special architecture or a supervised objective to do so. This work adopts an original approach based on a functional view of the network that exploits the sample's trajectories through the various layers and their statistical dependencies. It goes beyond multivariate features aggregation and introduces a baseline rooted in functional anomaly detection. In this new framework, OOD detection translates into detecting samples whose trajectories differ from the typical behavior characterized by the training set. We validate our method and empirically demonstrate its effectiveness in OOD detection compared to strong state-of-the-art baselines on computer vision benchmarks.

翻译：分布外检测的一个关键特性是利用训练好的神经网络，通过提取多层分类器中的统计模式和关系，来检测输入数据分布中预期的偏移。尽管现有先进方法取得了可靠结果，但其中部分方法仅依赖倒数第二层或最后一层的输出，忽略了分布外检测中富有价值的信息。探索多层的方法要么需要特殊架构，要么依赖监督目标才能实现。本文提出了一种基于网络功能性视角的新方法，该方法利用样本在各层中的轨迹及其统计依赖关系。它不仅超越了多变量特征聚合的范畴，还引入了一种根植于功能异常检测的基线方法。在这一新框架中，分布外检测转化为识别轨迹与训练集所定义的典型行为存在差异的样本。我们在计算机视觉基准上验证了该方法，并通过实验证明，与强力的先进基线方法相比，我们的方法在分布外检测中具有显著有效性。