As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for giving context to predictions. Out-of-distribution (OOD) detection methods have been created for this task. Such methods can be split into representation-based or logit-based methods from whether they respectively utilize the model's embeddings or predictions for OOD detection. In contrast to most papers which solely focus on one such group, we address both. We employ dimensionality reduction on feature embeddings in representation-based methods for both time speedups and improved performance. Additionally, we propose DICE-COL, a modification of the popular logit-based method Directed Sparsification (DICE) that resolves an unnoticed flaw. We demonstrate the effectiveness of our methods on the OpenOODv1.5 benchmark framework, where they significantly improve performance and set state-of-the-art results.

翻译：随着机器学习在影响深远的决策中日益普及，识别推理数据是否超出模型预期的输入分布，对于为预测结果提供上下文至关重要。为此，人们开发了分布外（OOD）检测方法。这些方法可分为基于表示的方法和基于对数的方法，前者利用模型的嵌入表示，后者利用模型的预测结果进行OOD检测。与大多数仅关注其中一类方法的论文不同，我们同时针对这两类方法进行了改进。在基于表示的方法中，我们利用特征嵌入的降维技术，既提升了处理速度，又改善了性能。此外，我们提出了DICE-COL，这是对流行的基于对数的方法——定向稀疏化（DICE）的改进，解决了其中未被察觉的缺陷。我们在OpenOODv1.5基准框架上验证了这些方法的有效性，结果显示它们显著提升了性能，并取得了最先进的结果。