Predictions of opaque black-box systems are frequently deployed in
high-stakes applications such as healthcare. For such applications, it is
crucial to assess how models handle samples beyond the domain of training data.
While several metrics and tests exist to detect out-of-distribution (OoD) data
from in-distribution (InD) data to a deep neural network (DNN), their
performance varies significantly across datasets, models, and tasks, which
limits their practical use. In this paper, we propose a hypothesis-driven
approach to quantify whether a new sample is InD or OoD. Given a trained DNN
and some input, we first feed the input through the DNN and compute an ensemble
of OoD metrics, which we term latent responses. We then formulate the OoD
detection problem as a hypothesis test between latent responses of different
groups, and use permutation-based resampling to infer the significance of the
observed latent responses under a null hypothesis. We adapt our method to
detect an unseen sample of bacteria to a trained deep learning model, and show
that it reveals interpretable differences between InD and OoD latent responses.
Our work has implications for systematic novelty detection and informed
decision-making from classifiers trained on a subset of labels.

我们提出了一种基于假设的方法来确定新样本是属于训练集范围内还是超出范围，在黑盒系统中，通过深度神经网络 (DNN) 计算超出范围 (Out-of-Distribution, OoD) 的隐含回应，并将 OoD 检测问题形式化为不同组的隐含回应之间的假设检验。我们将这种方法应用于一个训练好的深度学习模型，用以检测未知的细菌样本，并展示了其在 InD 和 OoD 隐含回应之间揭示了可解释的差异。该方法对于系统性的新颖性检测和基于子标签训练的分类器的明智决策具有重要意义。