Effective OOD detection is crucial for reliable machine learning models, yet
most current methods are limited in practical use due to requirements like
access to training data or intervention in training. We present a novel method
for detecting OOD data in deep neural networks based on transformation
smoothness between intermediate layers of a network (BLOOD), which is
applicable to pre-trained models without access to training data. BLOOD
utilizes the tendency of between-layer representation transformations of
in-distribution (ID) data to be smoother than the corresponding transformations
of OOD data, a property that we also demonstrate empirically for Transformer
networks. We evaluate BLOOD on several text classification tasks with
Transformer networks and demonstrate that it outperforms methods with
comparable resource requirements. Our analysis also suggests that when learning
simpler tasks, OOD data transformations maintain their original sharpness,
whereas sharpness increases with more complex tasks.

通过网络中间层之间的转换平滑度，我们提出了一种新的深度神经网络中 OOD 数据检测方法（BLOOD），适用于没有训练数据的预训练模型，并通过在 Transformer 网络上的多个文本分类任务中的评估证明其优越性。