Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-world scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground semantic features (e.g., vehicle images vs. ID samples in fruit classification) and background domain features (e.g., textural images vs. ID samples in object recognition). Existing methods focus on detecting OOD samples based on the semantic features, while neglecting the other dimensions such as the domain features. This paper considers the importance of the domain features in OOD detection and proposes to leverage them to enhance the semantic-feature-based OOD detection methods. To this end, we propose a novel generic framework that can learn the domain features from the ID training samples by a dense prediction approach, with which different existing semantic-feature-based OOD detection methods can be seamlessly combined to jointly learn the in-distribution features from both the semantic and domain dimensions. Extensive experiments show that our approach 1) can substantially enhance the performance of four different state-of-the-art (SotA) OOD detection methods on multiple widely-used OOD datasets with diverse domain features, and 2) achieves new SotA performance on these benchmarks.

翻译：检测分布外（OOD）输入是确保深度神经网络分类器在开放世界场景中安全部署的主要任务。OOD样本可能来自任意分布，并在多个维度上与分布内（ID）数据存在偏差，例如前景语义特征（如车辆图像与水果分类中的ID样本）和背景领域特征（如纹理图像与目标识别中的ID样本）。现有方法主要基于语义特征检测OOD样本，而忽略了领域特征等其他维度。本文考虑了领域特征在OOD检测中的重要性，并提出利用这些特征增强基于语义特征的OOD检测方法。为此，我们提出了一种新颖的通用框架，通过密集预测方法从ID训练样本中学习领域特征，该框架能够与多种现有的基于语义特征的OOD检测方法无缝结合，从而从语义和领域两个维度联合学习分布内特征。大量实验表明，我们的方法：1）能在多个具有多样化领域特征的广泛使用的OOD数据集上显著提升四种不同最先进（SotA）OOD检测方法的性能；2）在这些基准测试中实现了新的SotA性能。